Sixth International Conference on Spoken Language Processing
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.
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.