Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

1 code implementation • 15 Mar 2021 • Yasuaki Okuda, Ryo Ozaki, Tadahiro Taniguchi

The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i. e., they follow Zipf's law.

Language Modelling Time Series +1