Application of vector quantized hidden Markov modeling to telephone network based connected digit recognition

Connected digit speech recognition in the telephone network is becoming increasingly more important as the demand for speech technology becomes widespread. In the past few years, several highly successful techniques for recognizing spoken connected digit strings have been proposed. Although these techniques have been applied to non-telephone based speech [e.g. Texas Instruments database], they have produced high recognition performance. Further, similar levels of performances have been demonstrated using discrete density and continuous density based hidden Markov models (HMMs). The success of the vector quantized (VQ) modeling approach, in particular, is encouraging and rather important from the viewpoint of computational efficiency. This paper presents a study of connected digit recognition on telephone network based data using VQ HMMs. We investigate several factors affecting the error rate of VQ HMMs-such as maximum mutual information (MMI) training, sender modeling, and codebook size-and measure their contributions to recognition accuracy. The model architecture, number of states and transitions, is also optimized and its contribution to overall performance discussed