Phoneme acquisition model based on vowel imitation using recurrent neural network

Hisashi Kanda, Tetsuya Ogata, Toru Takahashi, Kazunori Komatani, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

A phoneme-acquisition system was developed using a computational model that explains the developmental process of human infants in the early period of acquiring language. There are two important findings in constructing an infant's acquisition of phonemes: (1) an infant's vowel like cooing tends to invoke utterances that are imitated by its caregiver, and (2) maternal imitation effectively reinforces infant vocalization. Therefore, we hypothesized that infants can acquire phonemes to imitate their caregivers' voices by trial and error, i. e., infants use self-vocalization experience to search for imitable and unimitable elements in their caregivers' voices. On the basis of this hypothesis, we constructed a phoneme acquisition process using interaction involving vowel imitation between a human and an infant model. Our infant model had a vocal tract system, called the Maeda model, and an auditory system implemented by using Mel-Frequency Cepstral Coefficients (MFCCs) through STRAIGHT analysis. We applied Recurrent Neural Network with Parametric Bias (RNNPB) to learn the experience of self-vocalization, to recognize the human voice, and to produce the sound imitated by the infant model. To evaluate imitable and unimitable sounds, we used the prediction error of the RNNPB model. The experimental results revealed that as imitation interactions were repeated, the formants of sounds imitated by our system moved closer to those of human voices, and our system could self-organize the same vowels in different continuous sounds. This suggests that our system can reflect the process of phoneme acquisition.

Original languageEnglish
Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Pages5388-5393
Number of pages6
DOIs
Publication statusPublished - 2009 Dec 11
Externally publishedYes
Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States
Duration: 2009 Oct 112009 Oct 15

Publication series

Name2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Conference

Conference2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
CountryUnited States
CitySt. Louis, MO
Period09/10/1109/10/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Control and Systems Engineering

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    Kanda, H., Ogata, T., Takahashi, T., Komatani, K., & Okuno, H. G. (2009). Phoneme acquisition model based on vowel imitation using recurrent neural network. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 (pp. 5388-5393). [5354825] (2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009). https://doi.org/10.1109/IROS.2009.5354825