Restructuring Gaussian mixture density functions in speaker-independent acoustic models

In continuous speech recognition featuring hidden Markov model (HMM), word N-gram and time-synchronous beam search, a local modeling mismatch in the HMM will often cause the recognition performance to degrade. To cope with this problem, this paper proposes a method of restructuring Gaussian mixture pdfs in a pre-trained speaker-independent HMM based on speech data. In this method, mixture components are copied and shared among multiple mixture pdfs with the tendency of local errors taken into account. The tendency is given by comparing the pre-trained HMM and speech data which was used in the pre-training. Experimental results prove that the proposed method can effectively restore local modeling mismatches and improve the recognition performance.