Dataset Information

Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

ABSTRACT: The present study explored whether a tool for automatic detection and recognition of interactions and child-directed speech (CDS) in preschool classrooms could be developed, validated, and applied to non-coded video recordings representing children's classroom experiences. Using first-person video recordings collected by 13 preschool children during a morning in their classrooms, we extracted high-level audiovisual features from recordings using automatic speech recognition and computer vision services from a cloud computing provider. Using manual coding for interactions and transcriptions of CDS as reference, we trained and tested supervised classifiers and linear mappings to measure five variables of interest. We show that the supervised classifiers trained with speech activity, proximity, and high-level facial features achieve adequate accuracy in detecting interactions. Furthermore, in combination with an automatic speech recognition service, the supervised classifier achieved error rates for CDS measures that are in line with other open-source automatic decoding tools in early childhood settings. Finally, we demonstrate our tool's applicability by using it to automatically code and transcribe children's interactions and CDS exposure vertically within a classroom day (morning to afternoon) and horizontally over time (fall to winter). Developing and scaling tools for automatized capture of children's interactions with others in the preschool classroom, as well as exposure to CDS, may revolutionize scientific efforts to identify precise mechanisms that foster young children's language development.

SUBMITTER: Gonzalez Villasanti H

PROVIDER: S-EPMC7688182 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

Gonzalez Villasanti Hugo H Justice Laura M LM Chaparro-Moreno Leidy Johana LJ Lin Tzu-Jung TJ Purtell Kelly K

PloS one 20201125 11

The present study explored whether a tool for automatic detection and recognition of interactions and child-directed speech (CDS) in preschool classrooms could be developed, validated, and applied to non-coded video recordings representing children's classroom experiences. Using first-person video recordings collected by 13 preschool children during a morning in their classrooms, we extracted high-level audiovisual features from recordings using automatic speech recognition and computer vision s ...[more]

PMID: 33237919

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Project description:ObjectivesCochlear implants (CIs) are remarkable in allowing individuals with severe to profound hearing loss to perceive speech. Despite these gains in speech understanding, however, CI users often struggle to perceive elements such as vocal emotion and prosody, as CIs are unable to transmit the spectro-temporal detail needed to decode affective cues. This issue becomes particularly important for children with CIs, but little is known about their emotional development. In a previous study, pediatric CI users showed deficits in voice emotion recognition with child-directed stimuli featuring exaggerated prosody. However, the large intersubject variability and differential developmental trajectory known in this population incited us to question the extent to which exaggerated prosody would facilitate performance in this task. Thus, the authors revisited the question with both adult-directed and child-directed stimuli.DesignVocal emotion recognition was measured using both child-directed (CDS) and adult-directed (ADS) speech conditions. Pediatric CI users, aged 7-19 years old, with no cognitive or visual impairments and who communicated through oral communication with English as the primary language participated in the experiment (n = 27). Stimuli comprised 12 sentences selected from the HINT database. The sentences were spoken by male and female talkers in a CDS or ADS manner, in each of the five target emotions (happy, sad, neutral, scared, and angry). The chosen sentences were semantically emotion-neutral. Percent correct emotion recognition scores were analyzed for each participant in each condition (CDS vs. ADS). Children also completed cognitive tests of nonverbal IQ and receptive vocabulary, while parents completed questionnaires of CI and hearing history. It was predicted that the reduced prosodic variations found in the ADS condition would result in lower vocal emotion recognition scores compared with the CDS condition. Moreover, it was hypothesized that cognitive factors, perceptual sensitivity to complex pitch changes, and elements of each child's hearing history may serve as predictors of performance on vocal emotion recognition.ResultsConsistent with our hypothesis, pediatric CI users scored higher on CDS compared with ADS speech stimuli, suggesting that speaking with an exaggerated prosody-akin to "motherese"-may be a viable way to convey emotional content. Significant talker effects were also observed in that higher scores were found for the female talker for both conditions. Multiple regression analysis showed that nonverbal IQ was a significant predictor of CDS emotion recognition scores while Years using CI was a significant predictor of ADS scores. Confusion matrix analyses revealed a dependence of results on specific emotions; for the CDS condition's female talker, participants had high sensitivity (d' scores) to happy and low sensitivity to the neutral sentences while for the ADS condition, low sensitivity was found for the scared sentences.ConclusionsIn general, participants had higher vocal emotion recognition to the CDS condition which also had more variability in pitch and intensity and thus more exaggerated prosody, in comparison to the ADS condition. Results suggest that pediatric CI users struggle with vocal emotion perception in general, particularly to adult-directed speech. The authors believe these results have broad implications for understanding how CI users perceive emotions both from an auditory communication standpoint and a socio-developmental perspective.

Project description:When acquiring language, young children may use acoustic spectro-temporal patterns in speech to derive phonological units in spoken language (e.g., prosodic stress patterns, syllables, phonemes). Children appear to learn acoustic-phonological mappings rapidly, without direct instruction, yet the underlying developmental mechanisms remain unclear. Across different languages, a relationship between amplitude envelope sensitivity and phonological development has been found, suggesting that children may make use of amplitude modulation (AM) patterns within the envelope to develop a phonological system. Here we present the Spectral Amplitude Modulation Phase Hierarchy (S-AMPH) model, a set of algorithms for deriving the dominant AM patterns in child-directed speech (CDS). Using Principal Components Analysis, we show that rhythmic CDS contains an AM hierarchy comprising 3 core modulation timescales. These timescales correspond to key phonological units: prosodic stress (Stress AM, ~2 Hz), syllables (Syllable AM, ~5 Hz) and onset-rime units (Phoneme AM, ~20 Hz). We argue that these AM patterns could in principle be used by naïve listeners to compute acoustic-phonological mappings without lexical knowledge. We then demonstrate that the modulation statistics within this AM hierarchy indeed parse the speech signal into a primitive hierarchically-organised phonological system comprising stress feet (proto-words), syllables and onset-rime units. We apply the S-AMPH model to two other CDS corpora, one spontaneous and one deliberately-timed. The model accurately identified 72-82% (freely-read CDS) and 90-98% (rhythmically-regular CDS) stress patterns, syllables and onset-rime units. This in-principle demonstration that primitive phonology can be extracted from speech AMs is termed Acoustic-Emergent Phonology (AEP) theory. AEP theory provides a set of methods for examining how early phonological development is shaped by the temporal modulation structure of speech across languages. The S-AMPH model reveals a crucial developmental role for stress feet (AMs ~2 Hz). Stress feet underpin different linguistic rhythm typologies, and speech rhythm underpins language acquisition by infants in all languages.

Dataset Information

Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

Publications

Automatized analysis of children's exposure to child-directed speech in reschool settings: Validation and application.

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