Segmenting acoustic signal with articulatory movement using Recurrent Neural Network for phoneme acquisition

— This paper proposes a computational model for phoneme acquisition by infants. Human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. One of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. The key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. That is, the ability to distinguish phonemes is learned by recognizing unstable points in the dynamics of continuous sound with articulatory movement. We have developed a vocal imitation system embodying the relationship between articulatory movements and sounds produced by the movements. To segment acoustic signal with articulatory movement, we apply the segmenting method to our system by Recurrent Neural Network with Parametric Bias (RNNPB). This method determines the multiple segmentation boundaries in a temporal sequence usin...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Hisashi Kanda, Tetsuya Ogata, Kazunori Komatani, Hiroshi G. Okuno
• Keywords: Articulatory Movement, IROS 2008, Phoneme Acquisition, Robotics, Speech Sound