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Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software—a popular system for measuring linguistic input—estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is challenging to do in a language- independent manner; the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input. We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We share an open-source implementation of ALICE for use by the language research community, enabling automatic phoneme, syllable, and word count estimation from child-centered audio recordings. [ABSTRACT FROM AUTHOR]

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification. [ABSTRACT FROM AUTHOR]

Automatic word count estimation (WCE) from audio recordings can be used to quantify the amount of verbal communication in a recording environment. One key application of WCE is to measure language input heard by infants and toddlers in their natural environments, as captured by daylong recordings from microphones worn by the infants. Although WCE is nearly trivial for high-quality signals in high-resource languages, daylong recordings are substantially more challenging due to the unconstrained acoustic environments and the presence of near- and far-field speech. Moreover, many use cases of interest involve languages for which reliable ASR systems or even well-defined lexicons are not available. A good WCE system should also perform similarly for low- and high-resource languages in order to enable unbiased comparisons across different cultures and environments. Unfortunately, the current state-of-the-art solution, the LENA system, is based on proprietary software and has only been optimized for American English, limiting its applicability. In this paper, we build on existing work on WCE and present the steps we have taken towards a freely available system for WCE that can be adapted to different languages or dialects with a limited amount of orthographically transcribed speech data. Our system is based on language-independent syllabification of speech, followed by a language-dependent mapping from syllable counts (and a number of other acoustic features) to the corresponding word count estimates. We evaluate our system on samples from daylong infant recordings from six different corpora consisting of several languages and socioeconomic environments, all manually annotated with the same protocol to allow direct comparison. We compare a number of alternative techniques for the two key components in our system: speech activity detection and automatic syllabification of speech. As a result, we show that our system can reach relatively consistent WCE accuracy across multiple corpora and languages (with some limitations). In addition, the system outperforms LENA on three of the four corpora consisting of different varieties of English. We also demonstrate how an automatic neural network-based syllabifier, when trained on multiple languages, generalizes well to novel languages beyond the training data, outperforming two previously proposed unsupervised syllabifiers as a feature extractor for WCE. [ABSTRACT FROM AUTHOR]

Highlights • The role of spectral tilt in sentence prominence was investigated. • Estimators for tilt of glottal source spectrum and surface spectrum were compared. • Multidimensional representations for spectral tilt were also investigated. • Robustness of different tilt estimators was tested for noisy and coded speech. • Experiments were conducted on both French and Dutch speech data. Abstract Linguistic prominence in speech is known to correlate with the acoustic measures of energy, F0, and duration. In contrast, the role of spectral tilt in the realization of prominence has remained more inconsistent between previous empirical investigations. This may be partially due to the lack of a standard method for quantifying spectral tilt or due to difficulties in estimating the acoustical source of spectral tilt, the glottal flow, from continuous speech. These issues have rendered interpretations and comparisons between studies difficult. In addition, (i) little is known about the robustness of tilt estimators for prominence detection in the case when speech is not clean but corrupted, as in real life, by environmental noise or telephone transmission (i.e. degradation caused by bandpass filtering and quantization noise). Moreover, (ii) little attention has been paid to multidimensional representations of source spectrum that can potentially incorporate more information about the phonation style than purely scalar measures. In this work, we study spectral tilt in signaling prominence in spoken Dutch and French under different levels of additive noise, and for telephone-band coded speech, and compare several one-dimensional tilt measures that have been previously encountered in the literature as well as multidimensional tilt measures. We also compare spectral tilt measures with other standard acoustic correlates for prominence, namely, energy, F0, and duration. Our results provide further empirical support for the finding that tilt is a systematic correlate of prominence in Dutch, that the role is smaller in French, and that energy, F0, and duration appear still to be the most robust features for discriminating prominent and non-prominent words. In addition, our results show that there are notable differences between different tilt measures at different levels of noise, and that multidimensional representations for tilt improve class separability from the scalar measures. [ABSTRACT FROM AUTHOR]

The exaggerated intonation and special rhythmic properties of infant-directed speech (IDS) have been hypothesized to attract infants' attention to the speech stream. However, there has been little work actually connecting the properties of IDS to models of attentional processing or perceptual learning. A number of such attention models suggest that surprising or novel perceptual inputs attract attention, where novelty can be operationalized as the statistical (un)predictability of the stimulus in the given context. Since prosodic patterns such as F0 contours are accessible to young infants who are also known to be adept statistical learners, the present paper investigates a hypothesis that F0 contours in IDS are less predictable than those in adult-directed speech (ADS), given previous exposure to both speaking styles, thereby potentially tapping into basic attentional mechanisms of the listeners in a similar manner that relative probabilities of other linguistic patterns are known to modulate attentional processing in infants and adults. Computational modeling analyses with naturalistic IDS and ADS speech from matched speakers and contexts show that IDS intonation has lower overall temporal predictability even when the F0 contours of both speaking styles are normalized to have equal means and variances. A closer analysis reveals that there is a tendency of IDS intonation to be less predictable at the end of short utterances, whereas ADS exhibits more stable average predictability patterns across the full extent of the utterances. The difference between IDS and ADS persists even when the proportion of IDS and ADS exposure is varied substantially, simulating different relative amounts of IDS heard in different family and cultural environments. Exposure to IDS is also found to be more efficient for predicting ADS intonation contours in new utterances than exposure to the equal amount of ADS speech. This indicates that the more variable prosodic contours of IDS also generalize to ADS, and may therefore enhance prosodic learning in infancy. Overall, the study suggests that one reason behind infant preference for IDS could be its higher information value at the prosodic level, as measured by the amount of surprisal in the F0 contours. This provides the first formal link between the properties of IDS and the models of attentional processing and statistical learning in the brain. However, this finding does not rule out the possibility that other differences between the IDS and ADS also play a role. [ABSTRACT FROM AUTHOR]

Syllables are often considered to be central to infant and adult speech perception. Many theories and behavioral studies on early language acquisition are also based on syllable-level representations of spoken language. There is little clarity, however, on what sort of pre-linguistic "syllable" would actually be accessible to an infant with no phonological or lexical knowledge. Anchored by the notion that syllables are organized around particularly sonorous (audible) speech sounds, the present study investigates the feasibility of speech segmentation into syllable-like chunks without any a priori linguistic knowledge. We first operationalize sonority as a measurable property of the acoustic input, and then use sonority variation across time, or speech rhythm, as the basis for segmentation. The entire process from acoustic input to chunks of syllable-like acoustic segments is implemented as a computational model inspired by the oscillatory entrainment of the brain to speech rhythm. We analyze the output of the segmentation process in three different languages, showing that the sonority fluctuation in speech is highly informative of syllable and word boundaries in all three cases without any language-specific tuning of the model. These findings support the widely held assumption that syllable-like structure is accessible to infants even when they are only beginning to learn the properties of their native language. [ABSTRACT FROM AUTHOR]

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large‐scale cumulative empirical data from infants, as quantified by meta‐analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta‐analytic evaluation of computational models. We exemplify the meta‐analytic model evaluation approach with two modeling experiments on infant‐directed speech preference and native/non‐native vowel discrimination. [ABSTRACT FROM AUTHOR]

When infants learn to pronounce speech sounds of their native language, they face the so-called correspondence problem – how to know which articulatory gestures lead to acoustic sounds that are recognized as native speech sounds by other speakers? Previous research suggests that infants might not learn to imitate their parents via autonomous babbling because direct evaluation of the acoustic similarity between the speech sounds of the two is not possible due to different spectral characteristics of the voices caused by differing vocal tract morphologies. We present a novel robust model of infant vowel imitation learning, following a hypothesis that an infant learns to match their productions to their caregiver's speech sounds when the caregiver imitates the infant's babbles. Adapting a cross-situational associative learning technique, evidently present in infant word learning, our simulated language learner can cope with ambiguity in caregiver's responses to babbling as well as with the imprecision of the articulatory gestures of the infant itself. Our fully online learning model also combines vocal exploration and imitative interaction into a single process. Learning performance is evaluated in experiments using Finnish adults as caregivers for a virtual infant, responding to the infant's babbles with lexical words and, after a learning stage, evaluating the quality of the vowels produced by the learner. After 1000 babble-response pairs, our virtual infant is seen to reach a satisfying vowel imitation accuracy of 70–80%. [ABSTRACT FROM AUTHOR]

Numerous studies have examined the acoustic correlates of sentential stress and its underlying linguistic functionality. However, the mechanism that connects stress cues to the listener's attentional processing has remained unclear. Also, the learnability versus innateness of stress perception has not been widely discussed. In this work, we introduce a novel perspective to the study of sentential stress and put forward the hypothesis that perceived sentence stress in speech is related to the unpredictability of prosodic features, thereby capturing the attention of the listener. As predictability is based on the statistical structure of the speech input, the hypothesis also suggests that stress perception is a result of general statistical learning mechanisms. To study this idea, computational simulations are performed where temporal prosodic trajectories are modeled with an n-gram model. Probabilities of the feature trajectories are subsequently evaluated on a set of novel utterances and compared to human perception of stress. The results show that the low-probability regions of F0 and energy trajectories are strongly correlated with stress perception, giving support to the idea that attention and unpredictability of sensory stimulus are mutually connected. [ABSTRACT FROM AUTHOR]

Automatic detection of prominence in speech has attracted interest in recent years due to its multiple uses in spoken language applications. However, typical approaches require manual labeling of the data that is an expensive and time consuming process, also making the systems potentially specific to the language or speaking style in question. In this paper, we propose a novel unsupervised algorithm for the automatic detection of sentence prominence named 3PRO (Prominence from Prosodic Probabilities; “ three-pro ”) that is based on recent findings on human perception of prominence in speech. By combining syllable duration information to the level of surprisal observed in the acoustic prosodic features, the method is capable of estimating prominent words from continuous speech without labeled training data. The algorithm is evaluated by comparing model output to manually transcribed prominence labels on a Dutch and French speech corpus, showing performance levels close to supervised prominence classifiers operating on the same data. [ABSTRACT FROM AUTHOR]

Human infants learn meanings for spoken words in complex interactions with other people, but the exact learning mechanisms are unknown. Among researchers, a widely studied learning mechanism is called cross-situational learning (XSL). In XSL, word meanings are learned when learners accumulate statistical information between spoken words and co-occurring objects or events, allowing the learner to overcome referential uncertainty after having sufficient experience with individually ambiguous scenarios. Existing models in this area have mainly assumed that the learner is capable of segmenting words from speech before grounding them to their referential meaning, while segmentation itself has been treated relatively independently of the meaning acquisition. In this article, we argue that XSL is not just a mechanism for word-to-meaning mapping, but that it provides strong cues for proto-lexical word segmentation. If a learner directly solves the correspondence problem between continuous speech input and the contextual referents being talked about, segmentation of the input into word-like units emerges as a by-product of the learning. We present a theoretical model for joint acquisition of proto-lexical segments and their meanings without assuming a priori knowledge of the language. We also investigate the behavior of the model using a computational implementation, making use of transition probability-based statistical learning. Results from simulations show that the model is not only capable of replicating behavioral data on word learning in artificial languages, but also shows effective learning of word segments and thenmeanings from continuous speech. Moreover, when augmented with a simple familiarity preference during learning, the model shows a good fit to human behavioral data in XSL tasks. These results support the idea of simultaneous segmentation and meaning acquisition and show that comprehensive models of early word segmentation should take referential word meanings into account. [ABSTRACT FROM AUTHOR]

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data. [ABSTRACT FROM AUTHOR]

This study focuses on feature selection in paralinguistic analysis and presents recently developed supervised and unsupervised methods for feature subset selection and feature ranking. Using the standard k -nearest-neighbors (kNN) rule as the classification algorithm, the feature selection methods are evaluated individually and in different combinations in seven paralinguistic speaker trait classification tasks. In each analyzed data set, the overall number of features highly exceeds the number of data points available for training and evaluation, making a well-generalizing feature selection process extremely difficult. The performance of feature sets on the feature selection data is observed to be a poor indicator of their performance on unseen data. The studied feature selection methods clearly outperform a standard greedy hill-climbing selection algorithm by being more robust against overfitting. When the selection methods are suitably combined with each other, the performance in the classification task can be further improved. In general, it is shown that the use of automatic feature selection in paralinguistic analysis can be used to reduce the overall number of features to a fraction of the original feature set size while still achieving a comparable or even better performance than baseline support vector machine or random forest classifiers using the full feature set. The most typically selected features for recognition of speaker likability, intelligibility and five personality traits are also reported. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model infants’ speech acquisition and emergence of the skill of imitation. [•] A virtual infant, LeVI, learns phonemes when its babbling is rewarded by a caregiver. [•] Imitation by the virtual caregiver teaches LeVI inverse auditory-to-phonetic mappings. [•] Imitations are not exact reproductions, and all 25 Finnish phonemes are learned. [•] LeVi learns to recognise the caregiver’s phonemes with an average accuracy of 88%. [Copyright &y& Elsevier]

Several psychoacoustic phenomena such as loudness perception, absolute thresholds of hearing, and perceptual grouping in time are affected by temporal integration of the signal in the auditory system. Similarly, the frequency resolution of the hearing system, often expressed in terms of critical bands, implies signal integration across neighboring frequencies. Although progress has been made in understanding the neurophysiological mechanisms behind these processes, the underlying reasons for the observed integration characteristics have remained poorly understood. The current work proposes that the temporal and spectral integration are a result of a system optimized for pattern detection from ecologically relevant acoustic inputs. This argument is supported by a simulation where the average time-frequency structure of speech that is derived from a large set of speech signals shows a good match to the time-frequency characteristics of the human auditory system. The results also suggest that the observed integration characteristics are learnable from acoustic inputs of the auditory environment using a Hebbian-like learning rule. [ABSTRACT FROM AUTHOR]

Abstract: The degree of interhemispheric synchrony in the neonatal EEG assessment refers to the co-occurrence of activity bouts during quiet sleep or burst suppression, and it has been widely considered as a key component in assessing background activity. However, no objective measures have been published for measuring it, and all conventionally used visual criteria suffer from significant ambiguities. Our present study aimed to develop such a quantitative measure of (a)synchrony, called activation synchrony index (ASI). We developed the ASI paradigm based on the testing of statistical independence between two quantized amplitude envelopes of wideband-filtered signals where higher frequencies had been pre-emphasized. The core parameter settings of ASI paradigm were defined using a smaller EEG dataset, and the final ASI paradigm was tested using a visually classified dataset of EEG records from 33 fullterm and 25 preterm babies, which showed varying grades of asynchrony. Our findings show that ASI could distinguish all EEG recordings with normal synchrony from those with modest or severe asynchrony at individual level, and there was a highly significant correlation (p <0.001) between ASI and the visually assessed grade of asynchrony. In addition, we showed that i) ASI is stable in recordings over several hours in duration, such as the typical neonatal brain monitoring, that ii) ASI values are sensitive to sleep stage, and that iii) they correlate with age in the preterm babies. Comparison of ASI to other three potential paradigms demonstrated a significant competitive advantage. Finally, ASI was found to be remarkably resistant to common artefacts as tested by adding significant level of real EEG artefacts from noisy recordings. An objective and reliable measure of (a)synchrony may open novel avenues for using ASI as a putative early functional biomarker in the neonatal brain, as well as for building proper automated classifiers of neonatal EEG background. Notably, the signature of synchrony of this kind, temporal coincidence of activity bouts, is a common feature in biological signals, suggesting that ASI may also hold promise as a useful paradigm for assessing temporal synchrony in other biosignals as well, such as muscle activities or movements. [Copyright &y& Elsevier]

Abstract: This work reviews a number of existing computational studies concentrated on the question of how spoken language can be learned from continuous speech in the absence of linguistically or phonetically motivated background knowledge, a situation faced by human infants when they first attempt to learn their native language. Specifically, the focus is on how phonetic categories and word-like units can be acquired purely on the basis of the statistical structure of speech signals, possibly aided by some articulatory or visual constraints. The outcomes and shortcomings of the existing work are reflected onto findings from experimental and theoretical studies. Finally, some of the open questions and possible future research directions related to the computational models of language acquisition are discussed. [Copyright &y& Elsevier]

Abstract: An efficient method for weakly supervised pattern discovery and recognition from discrete categorical sequences is introduced. The method utilizes two parallel sources of data: categorical sequences carrying some temporal or spatial information and a set of labeled, but not exactly aligned, contextual events related to the sequences. From these inputs the method builds associative models able to describe systematically co-occurring structures in the input streams. The learned models, based on transitional probabilities of events observed at several different time lags, inherently segment and classify novel sequences into contextual categories. Learning and recognition processes are purely incremental and computationally cheap, making the approach suitable for on-line learning tasks. The capabilities of the algorithm are demonstrated in a keyword learning task from continuous infant-directed speech and a continuous speech recognition task operating at varying noise levels. [Copyright &y& Elsevier]

Abstract: Word segmentation from continuous speech is a difficult task that is faced by human infants when they start to learn their native language. Several studies indicate that infants might use several different cues to solve this problem, including intonation, linguistic stress, and transitional probabilities between subsequent speech sounds. In this work, a computational model for word segmentation and learning of primitive lexical items from continuous speech is presented. The model does not utilize any a priori linguistic or phonemic knowledge such as phones, phonemes or articulatory gestures, but computes transitional probabilities between atomic acoustic events in order to detect recurring patterns in speech. Experiments with the model show that word segmentation is possible without any knowledge of linguistically relevant structures, and that the learned ungrounded word models show a relatively high selectivity towards specific words or frequently co-occurring combinations of short words. [Copyright &y& Elsevier]

Assessing infant carrying and holding (C/H), or physical infant-caregiver interaction, is important for a wide range of contexts in development research. An automated detection and quantification of infant C/H is particularly needed in long term at-home studies where development of infants' neurobehavior is measured using wearable devices. Here, we first developed a phenomenological categorization for physical infant-caregiver interactions to support five different definitions of C/H behaviors. Then, we trained and assessed deep learning-based classifiers for their automatic detection from multi-sensor wearable recordings that were originally used for mobile assessment of infants' motor development. Our results show that an automated C/H detection is feasible at few-second temporal accuracy. With the best C/H definition, the automated detector shows 96% accuracy and 0.56 kappa, which is slightly less than the video-based inter-rater agreement between trained human experts (98% accuracy, 0.77 kappa). The classifier performance varies with C/H definition reflecting the extent to which infants' movements are present in each C/H variant. A systematic benchmarking experiment shows that the widely used actigraphy-based method ignores the normally occurring C/H behaviors. Finally, we show proof-of-concept for the utility of the novel classifier in studying C/H behavior across infant development. Particularly, we show that matching the C/H detections to individuals' gross motor ability discloses novel insights to infant-parent interaction. [ABSTRACT FROM AUTHOR]

Abstract Purpose Method Result Conclusion The purpose of this study was to analyse the relationship between automatic vowel articulation index (aVAI) and direct magnitude estimation (DME) among speakers with Parkinson’s disease (PD) and healthy controls. We further analysed the potential of aVAI to serve as an objective measure of speech impairment in the clinical setting.Speech samples from native Finnish speakers were utilised. Expert raters utilised DME to scale the intelligibility of speech samples. aVAI scores for PD speakers and healthy control speakers were analysed in relationship to DME speech intelligibility ratings and, among PD speakers, disease stage utilising nonparametric statistical analysis.Mean DME intelligibility ratings were lower among PD speakers compared to healthy controls. Mean aVAI scores were nearly the same between speaker groups. DME intelligibility ratings and aVAI were strongly correlated within the PD speaker group. aVAI and DME intelligibility ratings were moderately correlated with disease stage as measured by the Hoehn and Yahr scale.aVAI was observed to be a promising tool for analysing vowel articulation in PD speakers. Further research is warranted on the application of aVAI as an objective measure of severity of speech impairment in the clinical setting, with varying patient populations and speech samples. [ABSTRACT FROM AUTHOR]

Perceptual prominence of linguistic units such as words has been earlier connected to the concepts of predictability and attentional orientation. One hypothesis is that low-probability prosodic or lexical content is perceived as prominent due to the surprisal and high information value associated with the stimulus. However, the existing behavioral studies have used stimulus manipulations that follow or violate typical linguistic patterns present in the listeners’ native language, i.e., assuming that the listeners have already established a model for acceptable prosodic patterns in the language. In the present study, we investigated whether prosodic expectations and the resulting subjective impression of prominence is affected by brief statistical adaptation to suprasegmental acoustic features in speech, also in the case where the prosodic patterns do not necessarily follow language-typical marking for prominence. We first exposed listeners to five minutes of speech with uneven distributions of falling and rising fundamental frequency (F0) trajectories on sentence-final words, and then tested their judgments of prominence on a set of new utterances. The results show that the probability of the F0 trajectory affects the perception of prominence, a less frequent F0 trajectory making a word more prominent independently of the absolute direction of F0 change. In the second part of the study, we conducted EEG-measurements on a set of new subjects listening to similar utterances with predominantly rising or falling F0 on sentence-final words. Analysis of the resulting event-related potentials (ERP) reveals a significant difference in N200 and N400 ERP-component amplitudes between standard and deviant prosody, again independently of the F0 direction and the underlying lexical content. Since N400 has earlier been associated with semantic processing of stimuli, this suggests that listeners implicitly track probabilities at the suprasegmental level and that predictability of a prosodic pattern during a word has an impact to the semantic processing of the word. Overall, the study suggests that prosodic markers for prominence are at least partially driven by the statistical structure of recently perceived speech, and therefore prominence perception could be based on statistical learning mechanisms similar to those observed in early word learning, but in this case operating at the level of suprasegmental acoustic features. [ABSTRACT FROM AUTHOR]

In order to study how early emotional experiences shape infant development, one approach is to analyze the emotional content of speech heard by infants, as captured by child-centered daylong recordings, and as analyzed by automatic speech emotion recognition (SER) systems. However, since large-scale daylong audio is initially unannotated and differs from typical speech corpora from controlled environments, there are no existing in-domain SER systems for the task. Based on existing literature, it is also unclear what is the best approach to deploy a SER system for a new domain. Consequently, in this study, we investigated alternative strategies for deploying a SER system for large-scale child-centered audio recordings from a neonatal hospital environment, comparing cross-corpus generalization, active learning (AL), and domain adaptation (DA) methods in the process. We first conducted simulations with existing emotion-labeled speech corpora to find the best strategy for SER system deployment. We then tested how the findings generalize to our new initially unannotated dataset. As a result, we found that the studied AL method provided overall the most consistent results, being less dependent on the specifics of the training corpora or speech features compared to the alternative methods. However, in situations without the possibility to annotate data, unsupervised DA proved to be the best approach. We also observed that deployment of a SER system for real-world daylong child-centered audio recordings achieved a SER performance level comparable to those reported in literature, and that the amount of human effort required for the system deployment was overall relatively modest. • Speech emotion recognition for real-life child-centered audio recordings. • Comparison of cross-corpus generalization, active learning, and domain adaptation. • The studied active learning method provided the most consistent results. • Using annotated data, active learning excelled. • Without annotated data, unsupervised domain adaptation excelled. [ABSTRACT FROM AUTHOR]

In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals. [ABSTRACT FROM AUTHOR]