Unsupervised Phoneme and Word Discovery From Multiple Speakers Using Double Articulation Analyzer and Neural Network With Parametric Bias

• Main Authors: Ryo Nakashima, Ryo Ozaki, Tadahiro Taniguchi
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-10-01
• Series: Frontiers in Robotics and AI
• Subjects: word discovery; phoneme discovery; parametric bias; Bayesian model; neural network
• Online Access: https://www.frontiersin.org/article/10.3389/frobt.2019.00092/full

This paper describes a new unsupervised machine-learning method for simultaneous phoneme and word discovery from multiple speakers. Phoneme and word discovery from multiple speakers is a more challenging problem than that from one speaker, because the speech signals from different speakers exhibit different acoustic features. The existing method, a nonparametric Bayesian double articulation analyzer (NPB-DAA) with deep sparse autoencoder (DSAE) only performed phoneme and word discovery from a single speaker. Extending NPB-DAA with DSAE to a multi-speaker scenario is, therefore, the research problem of this paper.This paper proposes the employment of a DSAE with parametric bias in the hidden layer (DSAE-PBHL) as a feature extractor for unsupervised phoneme and word discovery. DSAE-PBHL is designed to subtract speaker-dependent acoustic features and speaker-independent features by introducing parametric bias input to the DSAE hidden layer. An experiment demonstrated that DSAE-PBHL could subtract distributed representations of acoustic signals, enabling extraction based on the types of phonemes rather than the speakers. Another experiment demonstrated that a combination of NPB-DAA and DSAE-PBHL outperformed other available methods accomplishing phoneme and word discovery tasks involving speech signals with Japanese vowel sequences from multiple speakers.