Comparative experiments of several adaptation approaches to noisy speech recognition using stochastic trajectory models

Abstract

The paper describes experiments on noisy speech recognition, using acoustic models based on the framework of Stochastic Trajectory Models (STM). We present the theoretical framework of 4 different approaches dealing with speech model adaptation: model-specific linear regression, speech feature space transformation, noise and speech models combination, STM state-based filtering. Experiments are performed on a speaker-dependent, 1011 word continuous speech recognition application with a word-pair perplexity of 28, using vocabulary-independent acoustic training, context independent phone models, and in various noisy testing environments. To measure the performance of each approach, recognition rate variation is studied under different noise types and noise levels. Our results show that the linear regression approach significantly outperforms the other methods, for every tested noise types at medium SNRs (between 6 to 24 dB). For the Gaussian noise, with an SNR between 6 to 24 dB, we observe a reduction of the word error rate from 20% to 59% when the linear regression is used, compared to the other methods.