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To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings of audiobook comprehension with the deep-learning speech recognition system Whisper. We find that (1) Whisper provides a self-contained EEG model of an intermediary representational stage that reflects elements of prelexical and lexical representation and prediction; (2) EEG modeling is more accurate when informed by 5-10s of speech context, which traditional context invariant categorical models do not encode; (3) Deep Whisper layers encoding linguistic structure were more accurate EEG models of selectively attended speech in two-speaker "cocktail party" listening conditions than early layers encoding acoustics. No such layer depth advantage was observed for unattended speech, consistent with a more superficial level of linguistic processing in the brain. Author summary: Most people effortlessly can understand different speakers with distinct voices and accents, no matter whether they whisper, shout or are happy or sad. This effortlessness belies the remarkable computational challenge that our brains solve to transform such variable speech sounds into words. It is believed that our brains deal with this variability by categorizing speech sounds into a flow of phonemes and/or syllable units that are consistent, no matter how a word is spoken. Compelling supporting evidence has come from electrophysiological recordings of brain activity–colloquially known as brain waves—taken as people listen to speech. Scientists have trained computational models to predict brain wave fluctuations that correlate with sequences of phoneme categories. However, modeling only phoneme categories may miss key stages in the recognition process, including how sounds are mapped to phonemes, and phonemes to words. New deep learning speech models that are trained to recognize a diverse range of speech and speakers may offer new opportunities to provide more complete accounts of brain activity. This article reveals that these models indeed predict hitherto unexplained fluctuations in speech brain waves that reflect elements of sub-words and words, and shows that fluctuations are context sensitive, which may reflect the brain anticipating upcoming speech. [ABSTRACT FROM AUTHOR]

The "cocktail party problem", the challenge of isolating individual speech signals from a noisy mixture, has traditionally been addressed using statistical methods. However, deep neural networks (DNNs), with their ability to learn complex patterns, have emerged as superior solutions. DNNs excel at capturing intricate relationships between mixed audio signals and their respective speech sources, enabling them to effectively separate overlapping speech signals in challenging acoustic environments. Recent advances in speech separation systems have drawn inspiration from the brain's hierarchical sensory information processing, incorporating top-down attention mechanisms. The top-down attention network (TDANet) employs an encoder–decoder architecture with top-down attention to enhance feature modulation and separation performance. By leveraging attention signals from multi-scale input features, TDANet effectively modifies features across different scales using a global attention (GA) module in the encoder–decoder design. Local attention (LA) layers then convert these modulated signals into high-resolution auditory characteristics. In this study, we propose two key modifications to TDANet. First, we substitute the fully trainable convolutional encoder with a deterministic hand-crafted multi-phase gammatone filterbank (MP-GTF), which mimics human hearing. Experimental results demonstrated that this substitution yielded comparable or even slightly superior performance to the original TDANet with a trainable encoder. Second, we replace the single multi-head self-attention (MHSA) layer in the global attention module with a transformer encoder block consisting of multiple MHSA layers. To optimize GPU memory utilization, we introduce a parameter sharing mechanism, dubbed "Reverse Cycle", across layers in the transformer-based encoder. Our experimental findings indicated that these proposed modifications enabled TDANet to achieve competitive separation performance, rivaling state-of-the-art techniques, while maintaining superior computational efficiency. [ABSTRACT FROM AUTHOR]

The cocktail party problem refers to people’s auditory scene analysis in the complex auditory scene of everyday life in acoustics, which has also been addressed in modern literature. In this article, I define the literary cocktail party as a literary soundscape characterised by noise, with the dynamics of attention motivating its sound events and overall material relationality. Taking Virginia Woolf’s Between the Acts as an example, this article analyses how the interaction of words and sounds works with the dynamics of passive and active attention – two types of attention working in the bottom-up and top-down neural processes of the brain respectively. In different stages of the literary cocktail party, the development of sound events incorporates sonic materiality into the understanding of literary form, promotes the literary effect of defamiliarisation and creates relations in the soundscape. In the end, the article proposes ‘dip listening’ as a productive listening method for modern literary soundscape that is capable of not only generating an embodied sense of epiphany in daily experience but also creates the betweenness among various agencies in a shared tiredness. [ABSTRACT FROM AUTHOR]

The auditory system can selectively attend to the target source in complex environments, the phenomenon known as the "cocktail party" effect. However, the spatiotemporal dynamics of electrophysiological activity associated with auditory selective spatial attention (ASSA) remain largely unexplored. In this study, single‐source and multiple‐source paradigms were designed to simulate different auditory environments, and microstate analysis was introduced to reveal the electrophysiological correlates of ASSA. Furthermore, cortical source analysis was employed to reveal the neural activity regions of these microstates. The results showed that five microstates could explain the spatiotemporal dynamics of ASSA, ranging from MS1 to MS5. Notably, MS2 and MS3 showed significantly lower partial properties in multiple‐source situations than in single‐source situations, whereas MS4 had shorter durations and MS5 longer durations in multiple‐source situations than in single‐source situations. MS1 had insignificant differences between the two situations. Cortical source analysis showed that the activation regions of these microstates initially transferred from the right temporal cortex to the temporal–parietal cortex, and subsequently to the dorsofrontal cortex. Moreover, the neural activity of the single‐source situations was greater than that of the multiple‐source situations in MS2 and MS3, correlating with the N1 and P2 components, with the greatest differences observed in the superior temporal gyrus and inferior parietal lobule. These findings suggest that these specific microstates and their associated activation regions may serve as promising substrates for decoding ASSA in complex environments. [ABSTRACT FROM AUTHOR]

Multilingual speakers can find speech recognition in everyday environments like restaurants and open-plan offices particularly challenging. In a world where speaking multiple languages is increasingly common, effective clinical and educational interventions will require a better understanding of how factors like multilingual contexts and listeners' language proficiency interact with adverse listening environments. For example, word and phrase recognition is facilitated when competing voices speak different languages. Is this due to a "release from masking" from lower-level acoustic differences between languages and talkers, or higher-level cognitive and linguistic factors? To address this question, we created a "one-man bilingual cocktail party" selective attention task using English and Mandarin speech from one bilingual talker to reduce low-level acoustic cues. In Experiment 1, 58 listeners more accurately recognized English targets when distracting speech was Mandarin compared to English. Bilingual Mandarin–English listeners experienced significantly more interference and intrusions from the Mandarin distractor than did English listeners, exacerbated by challenging target-to-masker ratios. In Experiment 2, 29 Mandarin–English bilingual listeners exhibited linguistic release from masking in both languages. Bilinguals experienced greater release from masking when attending to English, confirming an influence of linguistic knowledge on the "cocktail party" paradigm that is separate from primarily energetic masking effects. Effects of higher-order language processing and expertise emerge only in the most demanding target-to-masker contexts. The "one-man bilingual cocktail party" establishes a useful tool for future investigations and characterization of communication challenges in the large and growing worldwide community of Mandarin–English bilinguals. [ABSTRACT FROM AUTHOR]

Statistical learning – the ability to extract distributional regularities from input – is suggested to be key to language acquisition. Yet, evidence for the human capacity for statistical learning comes mainly from studies conducted in carefully controlled settings without auditory distraction. While such conditions permit careful examination of learning, they do not reflect the naturalistic language learning experience, which is replete with auditory distraction – including competing talkers. Here, we examine how statistical language learning proceeds in a virtual cocktail party environment, where the to-be-learned input is presented alongside a competing speech stream with its own distributional regularities. During exposure, participants in the Dual Talker group concurrently heard two novel languages, one produced by a female talker and one by a male talker, with each talker virtually positioned at opposite sides of the listener (left/right) using binaural acoustic manipulations. Selective attention was manipulated by instructing participants to attend to only one of the two talkers. At test, participants were asked to distinguish words from part-words for both the attended and the unattended languages. Results indicated that participants' accuracy was significantly higher for trials from the attended vs. unattended language. Further, the performance of this Dual Talker group was no different compared to a control group who heard only one language from a single talker (Single Talker group). We thus conclude that statistical learning is modulated by selective attention, being relatively robust against the additional cognitive load provided by competing speech, emphasizing its efficiency in naturalistic language learning situations. [ABSTRACT FROM AUTHOR]

If a diplomatic cocktail party lasts two hours, does it equal the duration of peace? Or are there less simplistic conceptualizations of 'peace' and 'time' for the theorization of diplomatic practices? This article draws on a concept of temporality based on an irreversible before/after distinction, and combines it with the modern systems theoretical premise that every event within the diplomatic system occurs under the medium of peace, finding that: (a) peace is not a state of affairs, but a medium of communication; (b) peace has no chronometric duration, but it is temporally minimal, vanishing as soon as it appears; (c) peace needs constant reproduction, i.e. the system of diplomacy regenerates itself by continuously triggering further peace-mediated events; and (d) peace is both negative and positive, as it denotes a restraint of violent capacities but needs active communication. These insights offer a universal concept of 'peace' that is abstract enough to cover every interpolity tie. They also suggest further research opportunities, including into what the rapid acceleration of society means for the proliferation of ever-quicker, ever-more-trivial diplomatic practices. [ABSTRACT FROM AUTHOR]

One of the most intractable issues in contemporary digital signal processing, particularly with regards to blind source separation methods, is known as the cocktail party dilemma. This problem suppose there are many sensors record many signals at same time to produce many mixed signals. To solve this problem, one of an important methods used for this purpose is an Independent Component Analysis method. This method abbreviates in how separate mixed signals without any pre-knowledge about the mixing signals?. It treats on the statistical features of a mixing signals. This work introduces a novel method to solve the cocktail party problem, by using hybrid method from the Quantum Particle Swarm Optimization method and the Bell-Sejnowski neural method to enhance the performance of the Independent Component Analysis. In addition, the proposed method uses the Negentropy function to be the objective function of the optimization process. The proposed algorithm has been implemented on two cases of three really signals, with 8-KHz frequencies. The results of the separating process measured in two directions: firstly by comparing the results with other methods as Particle Swarm Optimization and the Quantum Particle Swarm Optimization, where the results appear that the proposed method appears very high results than other methods. Secondly, by using standard metrics as Absolute Value Correlation Coefficient, Signal to Distortion Ratio, and Signal to Noise Ratio. [ABSTRACT FROM AUTHOR]

Seeing the speaker’s face greatly improves our speech comprehension in noisy environments. This is due to the brain’s ability to combine the auditory and the visual information around us, a process known as multisensory integration. Selective attention also strongly influences what we comprehend in scenarios with multiple speakers–an effect known as the cocktail-party phenomenon. However, the interaction between attention and multisensory integration is not fully understood, especially when it comes to natural, continuous speech. In a recent electroencephalography (EEG) study, we explored this issue and showed that multisensory integration is enhanced when an audiovisual speaker is attended compared to when that speaker is unattended. Here, we extend that work to investigate how this interaction varies depending on a person’s gaze behavior, which affects the quality of the visual information they have access to. To do so, we recorded EEG from 31 healthy adults as they performed selective attention tasks in several paradigms involving two concurrently presented audiovisual speakers. We then modeled how the recorded EEG related to the audio speech (envelope) of the presented speakers. Crucially, we compared two classes of model – one that assumed underlying multisensory integration (AV) versus another that assumed two independent unisensory audio and visual processes (A+V). This comparison revealed evidence of strong attentional effects on multisensory integration when participants were looking directly at the face of an audiovisual speaker. This effect was not apparent when the speaker’s face was in the peripheral vision of the participants. Overall, our findings suggest a strong influence of attention on multisensory integration when high fidelity visual (articulatory) speech information is available. More generally, this suggests that the interplay between attention and multisensory integration during natural audiovisual speech is dynamic and is adaptable based on the specific task and environment. [ABSTRACT FROM AUTHOR]

The cocktail party problem can be more effectively addressed by leveraging the speaker's visual and audio information. This paper proposes a method to improve the audio's separation using two visual cues: facial features and lip movement. Firstly, residual connections are introduced in the audio separation module to extract detailed features. Secondly, considering the video stream contains information other than the face, which has a minimal correlation with the audio, an attention mechanism is employed in the face module to focus on crucial information. Then, the loss function considers the audio-visual similarity to take advantage of the relationship between audio and visual completely. Experimental results on the public VoxCeleb2 dataset show that the proposed model significantly enhanced SDR, PSEQ, and STOI, especially 4 dB improvements in SDR. [ABSTRACT FROM AUTHOR]

Motivated by experimental observations, we investigate a variant of the cocktail party problem: the detection of a weak periodic stimulus in the presence of fluctuations and another periodic stimulus which is stronger than the periodic signal to be detected. Specifically, we study the response of a population of stochastic leaky integrate-and-fire (LIF) neurons to two periodic signals and focus in particular on the question, whether the presence of one of the stimuli can be detected from the population activity. As a detection criterion, we use a simple threshold-crossing of the population activity over a certain time window. We show by means of the receiver operating characteristics (ROC) that the detectability depends only weakly on the time window of observation but rather strongly on the stimulus amplitude. Counterintuitively, the detection of the weak periodic signal can be facilitated by the presence of a strong periodic input current depending on the frequencies of the two signals and on the dynamical regime in which the neurons operate. Beside numerical simulations of the model, we present an analytical approximation for the ROC curve that is based on the weakly nonlinear response theory for a stochastic LIF neuron. [ABSTRACT FROM AUTHOR]

The Cocktail party problem, which is tracing and identifying a specific speaker's speech while numerous speakers communicate concurrently is one of the crucial problems still to be addressed for automated speech recognition (ASR) and speaker recognition. In this study, we attempt to thoroughly explore traditional methods for speech separation in a cocktail party environment and further analyze traditional single-channel methods for example source-driven methods like Computational Auditory Scene Analysis (CASA), data-driven methods like non-negative matrix factorization (NMF), model-driven methods, customary multi-channel methods such as beamforming, blind source separation for multi-channel and the newly developed deep learning approaches such as meta-learning based methods, self-supervised learning. This paper further accentuates numerous datasets and evaluation metrics in the domain of speech processing & brings out the comparison between traditional methods and methods based on deep learning for speech separation. This study provides a basic understanding and comprehensive knowledge of state-of-the-art researches in the area of speech separation and serves as a brief overview to new researchers. [ABSTRACT FROM AUTHOR]

A decomposition C ${\mathscr{C}}$ of a graph G $G$ is primitive if no proper, nontrivial subset of C ${\mathscr{C}}$ is a decomposition of an induced subgraph of G $G$. An unresolved question posed by Asplund et al. in a recent publication involves the existence of primitive decompositions of cocktail party graphs into cycles of length 4, which is resolved by this paper. [ABSTRACT FROM AUTHOR]

The well-known “cocktail party effect” refers to incidental detection of salient words, such as one’s own-name, in supposedly unattended speech. However, empirical investigation of the prevalence of this phenomenon and the underlying mechanisms has been limited to extremely artificial contexts and has yielded conflicting results. We introduce a novel empirical approach for revisiting this effect under highly ecological conditions, by immersing participants in a multisensory Virtual Café and using realistic stimuli and tasks. Participants (32 female, 18 male) listened to conversational speech from a character at their table, while a barista in the back of the café called out food orders. Unbeknownst to them, the barista sometimes called orders containing either their own-name or words that created semantic violations. We assessed the neurophysiological response-profile to these two probes in the task-irrelevant barista stream by measuring participants’ brain activity (EEG), galvanic skin response and overt gaze-shifts. [ABSTRACT FROM AUTHOR]

A graphical design is a subset of graph vertices such that the weighted averages of certain graph eigenvectors over the design agree with their global averages. We use Gale duality to show that positively weighted graphical designs in regular graphs are in bijection with the faces of a generalized eigenpolytope of the graph. This connection can be used to organize, compute and optimize designs. We illustrate the power of this tool on three families of Cayley graphs – cocktail party graphs, cycles, and graphs of hypercubes – by computing or bounding the smallest designs that average all but the last eigenspace in frequency order. [ABSTRACT FROM AUTHOR]

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception. When humans tune in to one talker in a "cocktail party" scenario, what do we do with the non-target speech? This human intracranial study reveals new insights into the distinct mechanisms by which listeners process target and non-target speech in a crowded environment. [ABSTRACT FROM AUTHOR]

Audio signal separation is an open and challenging issue in the classical "Cocktail Party Problem". Especially in a reverberation environment, the separation of mixed signals is more difficult separated due to the influence of reverberation and echo. To solve the problem, we propose a determined reverberant blind source separation algorithm. The main innovation of the algorithm focuses on the estimation of the mixing matrix. A new cost function is built to obtain the accurate demixing matrix, which shows the gap between the prediction and the actual data. Then, the update rule of the demixing matrix is derived using Newton gradient descent method. The identity matrix is employed as the initial demixing matrix for avoiding local optima problem. Through the real-time iterative update of the demixing matrix, frequency-domain sources are obtained. Then, time-domain sources can be obtained using an inverse short-time Fourier transform. Experimental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods. In particular, it has much better superiority in the highly reverberant environment. [ABSTRACT FROM AUTHOR]

Locating the sound source is one of the most important capabilities of robot audition. In recent years, single-source localization techniques have increasingly matured. However, localizing and tracking specific sound sources in multi-source scenarios, which is known as the cocktail party problem, is still unresolved. In order to address this challenge, in this paper, we propose a system for dynamically localizing and tracking sound sources based on audio–visual information that can be deployed on a mobile robot. Our system first locates specific targets using pre-registered voiceprint and face features. Subsequently, the robot moves to track the target while keeping away from other sound sources in the surroundings instructed by the motion module, which helps the robot gather clearer audio data of the target to perform downstream tasks better. Its effectiveness has been verified via extensive real-world experiments with a 20% improvement in the success rate of specific speaker localization and a 14% reduction in word error rate in speech recognition compared to its counterparts. [ABSTRACT FROM AUTHOR]

Let G be a simple, finite, undirected graph with n vertices. The main purpose of this paper introduces the concepts of the minimum covering Gutman Estrada index, the minimum covering Seidel Estrada index, the minimum covering distance Estrada index, the minimum covering Randić Estrada index, the minimum covering Harary Estrada index, and the minimum covering color Estrada index of a graph. First, we compute the new concepts for some of the graphs, such as cocktail party, star, crown, complete and complete bipartite. Moreover, we establish upper and lower bounds for the new concepts. [ABSTRACT FROM AUTHOR]

Language identification is the front end of multilingual speech-processing tasks. The study aims to enhance the accuracy of language identification in complex acoustic environments by proposing a multi-scale feature extraction method. This method replaces the baseline feature extraction network with a multi-scale feature extraction network (SE-Res2Net-CBAM-BILSTM) to extract multi-scale features. A multilingual cocktail party dataset was simulated, and comparative experiments were conducted with various models. The experimental results show that the proposed model achieved language identification accuracies of 97.6% for an Oriental language dataset and 75% for a multilingual cocktail party dataset Furthermore, comparative experiments show that our model outperformed three other models in the accuracy, recall, and F1 values. Finally, a comparison of different loss functions shows that the model performance was better when using focal loss. [ABSTRACT FROM AUTHOR]

In this paper, we compute the distance energy, degree sum energy, degree exponent energy and degree sum exponent distance energy of Cocktail party graph. [ABSTRACT FROM AUTHOR]

Network architectures and learning principles have been critical in developing complex cognitive capabilities in artificial neural networks (ANNs). Spiking neural networks (SNNs) are a subset of ANNs that incorporate additional biological features such as dynamic spiking neurons, biologically specified architectures, and efficient and useful paradigms. Here we focus more on network architectures in SNNs, such as themeta operator called 3-node networkmotifs, which is borrowed from the biological network. We proposed a Motif-topology improved SNN (M-SNN), which is further verified efficient in explaining key cognitive phenomenon such as the cocktail party effect (a typical noise-robust speech-recognition task) and McGurk effect (a typical multi-sensory integration task). For M-SNN, the Motif topology is obtained by integrating the spatial and temporal motifs. These spatial and temporal motifs are first generated from the pre-training of spatial (e.g., MNIST) and temporal (e.g., TIDigits) datasets, respectively, and then applied to the previously introduced two cognitive effect tasks. The experimental results showed a lower computational cost and higher accuracy and a better explanation of some key phenomena of these two effects, such as new concept generation and anti-background noise. This mesoscale network motifs topology has much room for the future. [ABSTRACT FROM AUTHOR]

Listeners use the spatial location or change in spatial location of coherent acoustic cues to aid in auditory object formation. From stimulus-evoked onset responses in normal-hearing listeners using electroencephalography (EEG), we have previously shown measurable tuning to stimuli changing location in quiet, revealing a potential window into the cortical representations of auditory scene analysis. These earlier studies used non-fluctuating, spectrally narrow stimuli, so it was still unknown whether previous observations would translate to speech stimuli, and whether responses would be preserved for stimuli in the presence of background maskers. To examine the effects that selective auditory attention and interferers have on object formation, we measured cortical responses to speech changing location in the free field with and without background babble (+6 dB SNR) during both passive and active conditions. Active conditions required listeners to respond to the onset of the speech stream when it occurred at a new location, explicitly indicating 'yes' or 'no' to whether the stimulus occurred at a block-specific location either 30 degrees to the left or right of midline. In the aggregate, results show similar evoked responses to speech stimuli changing location in quiet compared to babble background. However, the effect of the two background environments diverges somewhat when considering the magnitude and direction of the location change and where the subject was attending. In quiet, attention to the right hemifield appeared to evoke a stronger response than attention to the left hemifield when speech shifted in the rightward direction. No such difference was found in babble conditions. Therefore, consistent with challenges associated with cocktail party listening, directed spatial attention could be compromised in the presence of stimulus noise and likely leads to poorer use of spatial cues in auditory streaming. [ABSTRACT FROM AUTHOR]

The cocktail party problem refers to a challenging process when the human sensory system tries to separate a specific voice from a loud mixture of background sound sources. The problem is much more demanding for machines and has become the holy grail in robotic hearing. Despite many advances in noise suppression, the intrinsic information from the contaminated acoustic channel remains difficult to recover. Herein, a simple‐yet‐powerful laser‐assisted audio system termed robot ear accomplished by laser (REAL) is shown to probe the vibrations of sound‐carrying surfaces (mask, throat, and other nearby surfaces) in optical channel, which is intrinsically immune to acoustic background noises. The results demonstrate that REAL can directly obtain the audio‐frequency content from the laser without acoustic channel interference. The signals can be further transcribed into human‐recognizable audios by exploiting the internal time and frequency correlations through memory‐enabled neural networks. The REAL system would enable a new way in human–robot interaction. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.1002/aisy.202200143. [ABSTRACT FROM AUTHOR]

The human brain networks responsible for selectively listening to a voice amid other talkers remain to be clarified. The present study aimed to investigate relationships between cortical activity and performance in a speech-in-speech task, before (Experiment I) and after training-induced improvements (Experiment II). In Experiment I, 74 participants performed a speech-in-speech task while their cortical activity was measured using a functional near infrared spectroscopy (fNIRS) device. One target talker and one masker talker were simultaneously presented at three different target-to-masker ratios (TMRs): adverse, intermediate and favorable. Behavioral results show that performance may increase monotonically with TMR in some participants and failed to decrease, or even improved, in the adverse-TMR condition for others. On the neural level, an extensive brain network including the frontal (left prefrontal cortex, right dorsolateral prefrontal cortex and bilateral inferior frontal gyri) and temporal (bilateral auditory cortex) regions was more solicited by the intermediate condition than the two others. Additionally, bilateral frontal gyri and left auditory cortex activities were found to be positively correlated with behavioral performance in the adverse-TMR condition. In Experiment II, 27 participants, whose performance was the poorest in the adverse-TMR condition of Experiment I, were trained to improve performance in that condition. Results show significant performance improvements along with decreased activity in bilateral inferior frontal gyri, the right dorsolateral prefrontal cortex, the left inferior parietal cortex and the right auditory cortex in the adverse-TMR condition after training. Arguably, lower neural activity reflects higher efficiency in processing masker inhibition after speech-in-speech training. As speech-in-noise tasks also imply frontal and temporal regions, we suggest that regardless of the type of masking (speech or noise) the complexity of the task will prompt the implication of a similar brain network. Furthermore, the initial significant cognitive recruitment will be reduced following a training leading to an economy of cognitive resources. [ABSTRACT FROM AUTHOR]

Selective listening to cocktail-party speech involves a network of auditory and inferior frontal cortical regions. However, cognitive and motor cortical regions are differentially activated depending on whether the task emphasizes semantic or phonological aspects of speech. Here we tested whether processing of cocktail-party speech differs when participants perform a shadowing (immediate speech repetition) task compared to an attentive listening task in the presence of irrelevant speech. Participants viewed audiovisual dialogues with concurrent distracting speech during functional imaging. Participants either attentively listened to the dialogue, overtly repeated (i.e., shadowed) attended speech, or performed visual or speech motor control tasks where they did not attend to speech and responses were not related to the speech input. Dialogues were presented with good or poor auditory and visual quality. As a novel result, we show that attentive processing of speech activated the same network of sensory and frontal regions during listening and shadowing. However, in the superior temporal gyrus (STG), peak activations during shadowing were posterior to those during listening, suggesting that an anterior–posterior distinction is present for motor vs. perceptual processing of speech already at the level of the auditory cortex. We also found that activations along the dorsal auditory processing stream were specifically associated with the shadowing task. These activations are likely to be due to complex interactions between perceptual, attention dependent speech processing and motor speech generation that matches the heard speech. Our results suggest that interactions between perceptual and motor processing of speech relies on a distributed network of temporal and motor regions rather than any specific anatomical landmark as suggested by some previous studies. [ABSTRACT FROM AUTHOR]

Humans have an exceptional ability to extract specific audio streams of interest in a noisy environment; this is known as the cocktail party effect. It is widely accepted that this ability is related to selective attention, a mental process that enables individuals to focus on a particular object. Evidence suggests that sensory neurons can be modulated by top-down signals transmitted from the prefrontal cortex. However, exactly how the projection of attention signals to the cortex and subcortex influences the cocktail effect is unclear. We constructed computational models to study whether attentional modulation is more effective at earlier or later stages for solving the cocktail party problem along the auditory pathway. We modeled the auditory pathway using deep neural networks (DNNs), which can generate representational neural patterns that resemble the human brain. We constructed a series of DNN models in which the main structures were autoencoders. We then trained these DNNs on a speech separation task derived from the dichotic listening paradigm, a common paradigm to investigate the cocktail party effect. We next analyzed the modulation effects of attention signals during all stages. Our results showed that the attentional modulation effect is more effective at the lower stages of the DNNs. This suggests that the projection of attention signals to lower stages within the auditory pathway plays a more significant role than the higher stages in solving the cocktail party problem. This prediction could be tested using neurophysiological experiments. [ABSTRACT FROM AUTHOR]

In the cocktail party situation, people with normal hearing usually follow a single speaker among multiple concurrent ones. However, there is no agreement in the literature as to whether the background is segregated into multiple streams/speakers. The current study varied the number of concurrent speech streams and investigated target detection and memory for the contents of a target stream as well as the processing of distractors. A malevoiced target stream was either presented alone (single-speech), together with one male-voiced distractor (one-distractor), or a male- and a femalevoiced distractor (two-distractor). Behavioral measures of target detection and content tracking performance as well as target- and distractor detection related event-related brain potentials (ERPs) were assessed. We found that the N2 amplitude decreased whereas the P3 amplitude increased from the single-speech to the concurrent speech streams conditions. Importantly, the behavioral effect of distractors differed between the conditions with one vs. two distractor speech streams and the non-zero voltages in the N2 time window for distractor numerals and in the P3 time window for syntactic violations appearing in the non-target speech stream significantly differed between the one- and two-distractor conditions for the same (male) speaker. These results support the notion that the two background speech streams are segregated, as they show that distractors and syntactic violations appearing in the non-target streams are processed even when two speech non-target speech streams are delivered together with the target stream. [ABSTRACT FROM AUTHOR]

Age‐related differences in the processing of audiovisual speech in a multi‐talker environment were investigated analysing event‐related spectral perturbations (ERSPs), focusing on theta, alpha and beta oscillations that are assumed to reflect conflict processing, multisensory integration and attentional mechanisms, respectively. Eighteen older and 21 younger healthy adults completed a two‐alternative forced‐choice word discrimination task, responding to audiovisual speech stimuli. In a cocktail‐party scenario with two competing talkers (located at −15° and 15° azimuth), target words (/yes/or/no/) appeared at a pre‐defined (attended) position, distractor words at the other position. In two audiovisual conditions, acoustic speech was combined either with informative or uninformative visual speech. While a behavioural benefit for informative visual speech occurred for both age groups, differences between audiovisual conditions in the theta and beta band were only present for older adults. A stronger increase in theta perturbations for stimuli containing uninformative visual speech could be associated with early conflict processing, while a stronger suppression in beta perturbations for informative visual speech could be associated to audiovisual integration. Compared to the younger group, the older group showed generally stronger beta perturbations. No condition differences in the alpha band were found. Overall, the findings suggest age‐related differences in audiovisual speech integration in a multi‐talker environment. While the behavioural benefit of informative visual speech was unaffected by age, older adults had a stronger need for cognitive control when processing conflicting audiovisual speech input. Furthermore, mechanisms of audiovisual integration are differently activated depending on the informational content of the visual information. [ABSTRACT FROM AUTHOR]

Listening in an acoustically cluttered scene remains a difficult task for both machines and hearing-impaired listeners. Normal-hearing listeners accomplish this task with relative ease by segregating the scene into its constituent sound sources, then selecting and attending to a target source. An assistive listening device that mimics the biological mechanisms underlying this behavior may provide an effective solution for those with difficulty listening in acoustically cluttered environments (e.g., a cocktail party). Here, we present a binaural sound segregation algorithm based on a hierarchical network model of the auditory system. In the algorithm, binaural sound inputs first drive populations of neurons tuned to specific spatial locations and frequencies. The spiking response of neurons in the output layer are then reconstructed into audible waveforms via a novel reconstruction method. We evaluate the performance of the algorithm with a speech-on-speech intelligibility task in normal-hearing listeners. This two-microphone-input algorithm is shown to provide listeners with perceptual benefit similar to that of a 16-microphone acoustic beamformer. These results demonstrate the promise of this biologically inspired algorithm for enhancing selective listening in challenging multi-talker scenes. [ABSTRACT FROM AUTHOR]

The "cocktail party" problem—how a listener perceives speech in noisy environments—is typically studied using speech (multi-talker babble) or noise maskers. However, realistic cocktail party scenarios often include background music (e.g., coffee shops, concerts). Studies investigating music's effects on concurrent speech perception have predominantly used highly controlled synthetic music or shaped noise, which do not reflect naturalistic listening environments. Behaviorally, familiar background music and songs with vocals/lyrics inhibit concurrent speech recognition. Here, we investigated the neural bases of these effects. While recording multichannel EEG, participants listened to an audiobook while popular songs (or silence) played in the background at a 0 dB signal-to-noise ratio. Songs were either familiar or unfamiliar to listeners and featured either vocals or isolated instrumentals from the original audio recordings. Comprehension questions probed task engagement. We used temporal response functions (TRFs) to isolate cortical tracking to the target speech envelope and analyzed neural responses around 100 ms (i.e., auditory N1 wave). We found that speech comprehension was, expectedly, impaired during background music compared to silence. Target speech tracking was further hindered by the presence of vocals. When masked by familiar music, response latencies to speech were less susceptible to informational masking, suggesting concurrent neural tracking of speech was easier during music known to the listener. These differential effects of music familiarity were further exacerbated in listeners with less musical ability. Our neuroimaging results and their dependence on listening skills are consistent with early attentional-gain mechanisms where familiar music is easier to tune out (listeners already know the song's expectancies) and thus can allocate fewer attentional resources to the background music to better monitor concurrent speech material. [ABSTRACT FROM AUTHOR]

Source motion was examined as a cue for segregating concurrent speech or noise sources. In two different headphone-based tasks—motion detection (MD) and speech-on-speech masking (SI)—one source among three was designated as the target only by imposing sinusoidal variation in azimuth during the stimulus presentation. For MD, the lstener was asked which of the three concurrent sources was in motion during the trial. For SI, the listener was asked to report the words spoken by the moving speech source. MD performance improved as the amplitude of the sinusoidal motion (i.e., displacement in azimuth) increased over the range of values tested (±5° to ±30°) for both modulated noise and speech targets, with better performance found for speech. SI performance also improved as the amplitude of target motion increased. Furthermore, SI performance improved as word position progressed throughout the sentence. Performance on the MD task was correlated with performance on SI task across individual subjects. For the SI conditions tested here, these findings are consistent with the proposition that listeners first detect the moving target source, then focus attention on the target location as the target sentence unfolds. [ABSTRACT FROM AUTHOR]

Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

Objective: Humans are able to localize the source of a sound. This enables them to direct attention to a particular speaker in a cocktail party. Psycho-acoustic studies show that the sensory cortices of the human brain respond to the location of sound sources differently, and the auditory attention itself is a dynamic and temporally based brain activity. In this work, we seek to build a computational model which uses both spatial and temporal information manifested in EEG signals for auditory spatial attention detection (ASAD). Methods: We propose an end-to-end spatiotemporal attention network, denoted as STAnet, to detect auditory spatial attention from EEG. The STAnet is designed to assign differentiated weights dynamically to EEG channels through a spatial attention mechanism, and to temporal patterns in EEG signals through a temporal attention mechanism. Results: We report the ASAD experiments on two publicly available datasets. The STAnet outperforms other competitive models by a large margin under various experimental conditions. Its attention decision for 1-second decision window outperforms that of the state-of-the-art techniques for 10-second decision window. Experimental results also demonstrate that the STAnet achieves competitive performance on EEG signals ranging from 64 to as few as 16 channels. Conclusion: This study provides evidence suggesting that efficient low-density EEG online decoding is within reach. Significance: This study also marks an important step towards the practical implementation of ASAD in real life applications. [ABSTRACT FROM AUTHOR]

This paper is an attempt to address to the problem of native language in a mixed voice environment. G- Cocktail would aid these applications in identifying commands given in Gujarati, even from a mixed voice stream. There are two phases of G-cocktail in the first phase, it creates features after filtering the voices and in the second it trains and classifies the dataset. This trained dataset helps in recognizing the new voice signal. The challenge in training a native language is the availability of a small dataset. A single-word input is used in model and phrase benchmark dataset from Microsoft and the Linguistic Data Consortium for Indian Languages (LDC-IL). To overcome the over fitting problem due to smaller dataset we used CatBoost algorithm. And fine-tuned the classification model to avoid the over fitting issue. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC. They are derived from a type of cepstral representation of the audio clip (a nonlinear "spectrum-of-a-spectrum"). MFCC is good for human voices but noises in the sound makes it less productive. To avoid this shortcoming of MFCC, first filtered the voices are used and then calculated the MFCC. The most relevant features are retained to make it more robust. With MFCC features, the pitch of the voices is also added, as pitch could vary with regional changes, mood of the person, age, and knowledge of the language to the speaker. A voice print of the whole sound files is constructed and fed it as features to the classification model. For training and testing 70% and 30% ratio is used in algorithms like K-means, Naïve Bayes, and Light GBM. Proposed model is compared with given data set and results proved that G-cocktail using XBoost performed better than the others under the given scenario in all parameters. [ABSTRACT FROM AUTHOR]

The 43rd Annual International TESOL France Colloquium will be held in Paris on November 15-17, 2024. The event will feature plenary speakers Terry Small, Varinder Unlu, and Alicja Gałązka. The conference will cover a wide range of topics in English language teaching, including adult learners, young learners, business English, technology in the classroom, and more. Talks will be conducted in English or French, and the conference fee includes access to sessions, documents, gifts, and social events. TESOL France does not provide financial assistance or scholarships for travel or accommodation expenses. [Extracted from the article]

Humans have the remarkable ability to selectively focus on a single talker in the midst of other competing talkers. The neural mechanisms that underlie this phenomenon remain incompletely understood. In particular, there has been longstanding debate over whether attention operates at an early or late stage in the speech processing hierarchy. One way to better understand this is to examine how attention might differentially affect neurophysiological indices of hierarchical acoustic and linguistic speech representations. In this study, we do this by using encoding models to identify neural correlates of speech processing at various levels of representation. Specifically, we recorded EEG from fourteen human subjects (nine female and five male) during a “cocktail party” attention experiment. Model comparisons based on these data revealed phonetic feature processing for attended, but not unattended speech. Furthermore, we show that attention specifically enhances isolated indices of phonetic feature processing, but that such attention effects are not apparent for isolated measures of acoustic processing. These results provide new insights into the effects of attention on different prelexical representations of speech, insights that complement recent anatomic accounts of the hierarchical encoding of attended speech. Furthermore, our findings support the notion that, for attended speech, phonetic features are processed as a distinct stage, separate from the processing of the speech acoustics. [ABSTRACT FROM AUTHOR]

T. S. Eliot wrote The Family Reunion (1939) while he composed the Four Quartets, twelve years after his conversion to Anglo-Catholicism in 1927. The play erects a bridge between the author's early stage productions (Sweeney Agonistes, The Rock and Murder in the Cathedral) and the later society plays (The Cocktail Party, The Confidential Clerk and The Elder Statesman). Moreover, in his 1951 essay "Poetry and Drama," Eliot delves into the strengths and weaknesses of The Family Reunion and links this play with his own experiments with a "conversational line" which introduces popular theatrical conventions wrapped in the patterns of poetry that may appeal to theatre-goers. This paper deals with Eliot's proposals for verse drama present in an incipient form in The Family Reunion, and explores the possible ways of translating these resources into Spanish. It focuses on Eliot's own explanations in "Poetry and Drama" about the way in which to adapt blank verse to the stage. Furthermore, this essay explores metrical concepts such as stress and caesura and applies them to wider aspects concerning dramatic patterns and inner/outer voice in characters. [ABSTRACT FROM AUTHOR]