8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

Model Composition by Lagrange Polynomial Approximation for Robust Speech Recognition in Noisy Environment

Chandra Kant Raut, Takuya Nishimoto, Shigeki Sagayama

The University of Tokyo, Japan

This paper presents a technique for estimating HMM model parameters for noisy speech from given clean speech HMM and noise HMM. The model parameters are estimated by approximating the non-linear function governing the relationship between speech and noise, by a Lagrange polynomial, and thus enabling the distribution of corrupted speech parameters to have a closed form. The method is computationally efficient, and the experimental results showed significant improvement in recognition performance of noisy speech with this approach. Typically, word accuracy increased from 9.2% with clean model to 82.8% with the model composed by the proposed method as compared to 45.4% with the model composed by PMC Log-normal approximation, on an isolated word recognition task for exhibition hall noise added at 10 dB SNR.

Full Paper

Bibliographic reference.  Raut, Chandra Kant / Nishimoto, Takuya / Sagayama, Shigeki (2004): "Model composition by lagrange polynomial approximation for robust speech recognition in noisy environment", In INTERSPEECH-2004, 2809-2812.