Construction method of acoustic models dealing with various background noises based on combination of HMMs

Background noise is one of the biggest problem for speech recognition systems in real environments. In order to achieve high recognition performance for corrupted speech, we proposed a new construction method of HMMs dealing with various kinds of background noise. At first, each HMM dealing with a single noise is trained for each background noise, and then all Gaussian components of those HMMs are combined into a "multi-mixture HMM". From the experimental results, the multi-mixture HMM gave the highest recognition performance for any kind of noise and any variation of SNR. Although the multi-mixture HMMs has high performance, it has a huge number of Gaussian components that makes the speech recognition slower. In order to solve the problem, we also proposed a reduction method of Gaussian components. It can decrease the number of Gaussian components with slight deterioration of recognition performance.