Fast output probability computation using scalar quantization and independent dimension multi-mixture

In this paper, we propose a high speed output probability computation algorithm for multi-mixture continuous HMM. In the algorithm, we adopted the following three techniques: 1) independent dimension multi-mixture computation (IDMM), 2) scalar quantization (SQ) and 3) output probability recalculation. At the first step of the algorithm, the state probability is roughly estimated using the IDMM and SQ. The IDMM is an approximate computation of the multi-mixture probability density function, which realizes the fast probability estimation along with the SQ. The result of the rough estimation is used to select states with the high output probability. Then, the rigid probability calculation is carried out on the selected states. Our experiment on speaker independent continuous speech recognition showed that the proposed algorithm saves 81% of time for the output probability computation and 71% in the total speech recognition process, without degradation of the recognition rate