Fast speech recognition algorithm under noisy environment using modified CMS-PMC and improved IDMM+SQ

We describe a fast speech recognition algorithm under a noisy environment. To achieve accurate and fast speech recognition under a noisy environment, a very fast speech recognition algorithm with well-adapted model against the noisy environment is required. First, for the model adaptation, we propose the MCMS-PMC: an integration of parallel model combination (PMC) and modified cepstral mean subtraction (MCMS) which estimates the cepstrum mean by taking account of the additive noise. Then, for the fast speech recognition, we propose new techniques to create the noise-adapted scalar quantized codebook in order to introduce the MCMS-PMC into the IDMM+SQ, which we proposed previously as a fast speech recognition algorithm using the scalar quantization approach. Finally, an effect of the proposed method is shown through the speaker-independent telephone-bandwidth continuous speech recognition experiment