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A COMPARISON STUDY ON INFANT-PARENT VOICE DIARIZATION.

Junzhe Zhu1, Mark Hasegawa-Johnson1, Nancy McElwain1

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Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
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PubMed
Summary

We developed a new framework for analyzing infant vocalizations, outperforming existing software in voice diarization. This advancement improves the study of early child language development.

Keywords:
Child SpeechLanguage DevelopmentMultiple Instance LearningSpeaker DiarizationTransfer LearningVoice Activity Detection

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Area of Science:

  • Developmental psychology
  • Speech and language pathology
  • Computational linguistics

Background:

  • Studying prelinguistic child voice is crucial for understanding early language acquisition.
  • Existing methods for voice analysis in infants have limitations in accuracy and scope.
  • Automated analysis of child vocalizations requires robust diarization techniques.

Purpose of the Study:

  • To design and evaluate a novel framework for studying prelinguistic child voice (3-24 months).
  • To improve the accuracy of voice diarization in infant vocalizations using advanced algorithms.
  • To identify optimal system components and training strategies for enhanced performance.

Main Methods:

  • Developed a diarization framework with a time-invariant feature extractor, context-dependent embedding generator, and classifier.
  • Investigated the impact of different system components and loss functions on performance.
  • Implemented a multiple-instance learning technique for pre-training on datasets with coarse labels.
  • Utilized convolutional feature extractors and compared them with log-mel features.

Main Results:

  • The best system achieved a 43.8% DER (speaker diarization error rate), significantly outperforming LENA software (55.4% DER).
  • Employing a convolutional feature extractor markedly enhanced neural diarization performance compared to log-mel features.
  • The multiple-instance learning approach enabled effective pre-training on larger, less precisely labeled datasets.

Conclusions:

  • The proposed framework offers a significant improvement in automated analysis of prelinguistic child voice.
  • Convolutional feature extractors are more effective than log-mel features for infant voice diarization.
  • Advanced machine learning techniques, including multiple-instance learning, are valuable for analyzing developmental speech data.