Phoneme acquisition model based on vowel imitation using recurrent neural network

Authors:
Hisashi Kanda;Tetsuya Ogata;Toru Takahashi;Kazunori Komatani;Hiroshi G. Okuno
Affiliations:
Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan;Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan;Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan;Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan;Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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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.