Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Analysis of Acoustic Models Trained on a Large-Scale Japanese Speech Database

Tomoko Matsui, Masaki Naito, Yoshinori Sagisaka, Kozo Okuda, Satoshi Nakamura

ATR Spoken Language Translation Research Labs. Soraku-gun, Kyoto, Japan

This paper investigates the performance of speaker-independent (SI) acoustic hidden Markov models (HMMs) trained with a huge Japanese speech database, and discusses the eciency and task-independency involved. The database consists of read and spontaneous speech uttered by 3,771 speakers. The speech involves wide distributions with respect to region and age to capture the Japanese speech characteristics as best as possible. Recognition experiments using the spontaneous speech show that task-independent acoustic models can be created when training data with a huge number of speakers is available.


Full Paper

Bibliographic reference.  Matsui, Tomoko / Naito, Masaki / Sagisaka, Yoshinori / Okuda, Kozo / Nakamura, Satoshi (2000): "Analysis of acoustic models trained on a large-scale Japanese speech database", In ICSLP-2000, vol.2, 503-506.