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Drug addiction can cause severe damage to the human brain, leading to significant problems in cognitive processing, such as irritability, speech distortions, and exaggeration of negative stimuli. Speech plays a fundamental role in social interaction, including both the production and perception. The ability to perceive communicative functions conveyed through speech is crucial for successful interpersonal communication and the maintaining good social relationships. However, due to the limited number of previous studies, it remains unclear whether the cognitive disorder caused by drug addiction affects the perception of communicative function conveyed in Mandarin speech. To address this question, we conducted a perception experiment involving sixty male participants, including 25 heroin addicts and 35 healthy controls. The experiment aimed to examine the perception of three communicative functions (i.e., statement, interrogative, and imperative) under three background noise conditions (i.e., no noise, SNR [Signal to Noise Ratio] = 10, and SNR = 0). Eight target sentences were first recorded by two native Mandarin speakers for each of the three communicative functions. Each half was then combined with Gaussian White Noise under two background noise conditions (i.e., SNR = 10 and SNR = 0). Finally, 48 speech stimuli were included in the experiment with four options provided for perceptual judgment. The results showed that, under the three noise conditions, the average perceptual accuracies of the three communicative functions were 80.66% and 38% for the control group and the heroin addicts, respectively. Significant differences were found in the perception of the three communicative functions between the control group and the heroin addicts under the three noise conditions, except for the recognition of imperative under strong noise condition (i.e., SNR = 0). Moreover, heroin addicts showed good accuracy (around 50%) in recognizing imperative and poor accuracy (i.e., lower than the chance level) in recognizing interrogative. This paper not only fills the research gap in the perception of communicative functions in Mandarin speech among drug addicts but also enhances the understanding of the effects of drugs on speech perception and provides a foundation for the speech rehabilitation of drug addicts., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Geng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the essential spectral features and spectral regions. The proposed LSTM model is applied to speech enhancement and recognition applications. The proposed LSTM model uses no future information, resulting in a causal system suitable for real-time processing. The combined spectral feature sets are used to train the LSTM model for improved performance. Using the proposed model, the ideal ratio mask (IRM) is estimated as a training objective. The experimental evaluations using short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) have demonstrated that the proposed model with robust feature representation obtained higher speech intelligibility and perceptual quality. With the TIMIT, LibriSpeech, and VoiceBank datasets, the proposed model improved STOI by 16.21%, 16.41%, and 18.33% over noisy speech, whereas PESQ is improved by 31.1%, 32.9%, and 32%. In seen and unseen noisy situations, the proposed model outperformed existing deep neural networks (DNNs), including baseline LSTM, feedforward neural network (FDNN), convolutional neural network (CNN), and generative adversarial network (GAN). With the Kaldi toolkit for automated speech recognition (ASR), the proposed model significantly reduced the word error rates (WERs) and reached an average WER of 15.13% in noisy backgrounds. [ABSTRACT FROM AUTHOR]

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications. [ABSTRACT FROM AUTHOR]

Objectives: Current evidence supports the benefits of cochlear implants (CIs) in children with hearing loss, including those with auditory neuropathy spectrum disorder (ANSD). However, there is limited evidence regarding factors that hold predictive value for intervention outcomes. Design: This retrospective case-control study consisted of 66 children with CIs, including 22 with ANSD and 44 with sensorineural hearing loss (SNHL) matched on sex, age, age at CI activation, and the length of follow-up with CIs (1:2 ratio). The case and control groups were compared in the results of five open-set speech perception tests, and a Forward Linear Regression Model was used to identify factors that can predict the post-CI outcomes. Results: There was no significant difference in average scores between the two groups across five outcome measures, ranging from 88.40% to 95.65%. The correlation matrix revealed that younger ages at hearing aid fitting and CI activation positively influenced improvements in speech perception test scores. Furthermore, among the variables incorporated in the regression model, the duration of follow-up with CIs, age at CI activation, and the utilization of two CIs demonstrated prognostic significance for improved post-CI speech perception outcomes. Conclusions: Children with ANSD can achieve similar open-set speech perception outcomes as children with SNHL. A longer CI follow-up, a lower age at CI activation, and the use of two CIs are predictive for optimal CI outcome. [ABSTRACT FROM AUTHOR]

Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods often face issues in recognizing. This article introduces three solutions based on convolutional neural networks (CNN) to solve the problem: 1D-CNN is designed to learn directly from digital data; 2DS-CNN and 2DM-CNN have a more complex architecture, transferring raw waveform into transformed images using Fourier transform to learn essential features. Experimental results on four large data sets, containing 30,000 samples for each, show that the three proposed models achieve superior performance compared to well-known models such as GoogLeNet and AlexNet, with the best accuracy of 95.87%, 99.65%, and 99.76%, respectively. With 5-10% higher performance than other models, the proposed solution has demonstrated the ability to effectively learn features, improve recognition accuracy and speed, and open up the potential for broad applications in virtual assistants, medical recording, and voice commands. [ABSTRACT FROM AUTHOR]

Evoked potential studies have shown that speech planning modulates auditory cortical responses. The phenomenon's functional relevance is unknown. We tested whether, during this time window of cortical auditory modulation, there is an effect on speakers' perceptual sensitivity for vowel formant discrimination. Participants made same/different judgments for pairs of stimuli consisting of a pre-recorded, self-produced vowel and a formant-shifted version of the same production. Stimuli were presented prior to a "go" signal for speaking, prior to passive listening, and during silent reading. The formant discrimination stimulus /uh/ was tested with a congruent productions list (words with /uh/) and an incongruent productions list (words without /uh/). Logistic curves were fitted to participants' responses, and the just-noticeable difference (JND) served as a measure of discrimination sensitivity. We found a statistically significant effect of condition (worst discrimination before speaking) without congruency effect. Post-hoc pairwise comparisons revealed that JND was significantly greater before speaking than during silent reading. Thus, formant discrimination sensitivity was reduced during speech planning regardless of the congruence between discrimination stimulus and predicted acoustic consequences of the planned speech movements. This finding may inform ongoing efforts to determine the functional relevance of the previously reported modulation of auditory processing during speech planning. [ABSTRACT FROM AUTHOR]

Cross-linguistic perception is known to be molded by native and second language (L2) experiences. Yet, the role of prosodic patterns and individual characteristics on how speakers of tonal languages perceive L2 Spanish sentence modalities remains relatively underexplored. This study addresses the gap by analyzing the auditory performance of 75 Mandarin speakers with varying levels of Spanish proficiency. The experiment consisted of four parts: the first three collected sociolinguistic profiles and assessed participants' pragmatic competence and musical abilities. The last part involved an auditory gating task, where participants were asked to identify Spanish broad focus statements and information-seeking yes/no questions with different stress patterns. Results indicated that the shape of intonation contours and the position of the final stressed syllable significantly impact learners' perceptual accuracy, with effects modulated by utterance length and L2 proficiency. Moreover, individual differences in pragmatic and musical competence were found to refine auditory and cognitive processing in Mandarin learners, thereby influencing their ability to discriminate question-statement contrasts. These findings reveal the complex interplay between prosodic and individual variations in L2 speech perception, providing novel insights into how speakers of tonal languages process intonation in a non-native Romance language like Spanish. [ABSTRACT FROM AUTHOR]

A hearing aid or a contralateral routing of signal device are options for unilateral cochlear implant listeners with limited hearing in the unimplanted ear; however, it is uncertain which device provides greater benefit beyond unilateral listening alone. Eighteen unilateral cochlear implant listeners participated in this prospective, within-participants, repeated measures study. Participants were tested with the cochlear implant alone, cochlear implant + hearing aid, and cochlear implant + contralateral routing of signal device configurations with a one-month take-home period between each in-person visit. Audiograms, speech perception in noise, and lateralization were evaluated. Subjective feedback was obtained via questionnaires. Marked improvement in speech in noise and non-implanted ear lateralization accuracy were observed with the addition of a contralateral hearing aid. There were no significant differences in speech recognition between listening configurations. However, the chronic device use questionnaires and the final device selection showed a clear preference for the hearing aid in spatial awareness and communication domains. Individuals with limited hearing in their unimplanted ears demonstrate significant improvement with the addition of a contralateral device. Subjective questionnaires somewhat contrast with clinic-based outcome measures, highlighting the delicate decision-making process involved in clinically advising one device or another to maximize communication benefits. [ABSTRACT FROM AUTHOR]

Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech processing can be investigated by measuring their electrical activity using scalp-mounted electrodes. However, typical analysis methods involve averaging neural responses to many short repetitive stimuli that bear little relevance to daily listening environments. Recently, subcortical responses to more ecologically relevant continuous speech were detected using linear encoding models. These methods estimate the temporal response function (TRF), which is a regression model that minimises the error between the measured neural signal and a predictor derived from the stimulus. Using predictors that model the highly non-linear peripheral auditory system may improve linear TRF estimation accuracy and peak detection. Here, we compare predictors from both simple and complex peripheral auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 participants listening to continuous speech. We also investigate the data length required for estimating subcortical TRFs, and find that around 12 minutes of data is sufficient for clear wave V peaks (>3 dB SNR) to be seen in nearly all participants. Interestingly, predictors derived from simple filterbank-based models of the peripheral auditory system yield TRF wave V peak SNRs that are not significantly different from those estimated using a complex model of the auditory nerve, provided that the nonlinear effects of adaptation in the auditory system are appropriately modelled. Crucially, computing predictors from these simpler models is more than 50 times faster compared to the complex model. This work paves the way for efficient modelling and detection of subcortical processing of continuous speech, which may lead to improved diagnosis metrics for hearing impairment and assistive hearing technology. [ABSTRACT FROM AUTHOR]

Given the increasing prevalence of online data collection, it is important to know how behavioral data obtained online compare to samples collected in the laboratory. This study compares online and in-person measurement of speech perception in older children and adolescents. Speech perception is important for assessment and treatment planning in speech-language pathology; we focus on the American English /ɹ/ sound because of its frequency as a clinical target. Two speech perception tasks were adapted for web presentation using Gorilla: identification of items along a synthetic continuum from rake to wake, and category goodness judgment of English /ɹ/ sounds in words produced by various talkers with and without speech sound disorder. Fifty typical children aged 9–15 completed these tasks online using a standard headset. These data were compared to a previous sample of 98 typical children aged 9–15 who completed the same tasks in the lab setting. For the identification task, participants exhibited smaller boundary widths (suggestive of more acute perception) in the in-person setting relative to the online setting. For the category goodness judgment task, there was no statistically significant effect of modality. The correlation between scores on the two tasks was significant in the online setting but not in the in-person setting, but the difference in correlation strength was not statistically significant. Overall, our findings agree with previous research in suggesting that online and in-person data collection do not yield identical results, but the two contexts tend to support the same broad conclusions. In addition, these results suggest that online data collection can make it easier for researchers connect with a more representative sample of participants. [ABSTRACT FROM AUTHOR]