8th International Conference on Spoken Language Processing

Jeju Island, Korea
October 4-8, 2004

HMM-Based Feature Compensation Method: An Evaluation Using the AURORA2

Akira Sasou (1), Kazuyo Tanaka (2), Satoshi Nakamura (3), Futoshi Asano (1)

(1) National Institute of Advanced Industrial Science and Technology (AIST), Japan
(2) University of Tsukuba, Japan
(3) ATR, Japan

In this paper, we describe an HMM-based feature-compensation method. The proposed method compensates for noise-corrupted features in the MFCC domain using the output probability density functions (pdf) of the Hidden Markov Models (HMM). In compensating the features, the output pdfs are adaptively weighted according to forward path probabilities. Because of this, the proposed method can minimize degradation of feature-compensation accuracy due to temporally changing noise environment. We evaluated the proposed method based on the AURORA2 database. All the experiments were conducted in a clean condition. The experiment results indicate that the proposed method, combined with cepstral mean subtraction, can achieve a word accuracy of 87.64%. We also show that the proposed method is useful in a transient pulse noise environment.

Full Paper

Bibliographic reference.  Sasou, Akira / Tanaka, Kazuyo / Nakamura, Satoshi / Asano, Futoshi (2004): "HMM-based feature compensation method: an evaluation using the AURORA2", In INTERSPEECH-2004, 121-124.