汉语连续语音中HMM模型状态数优化方法研究

The Optimal Selecting for HMM State-number in Mandarin Continuous Speech

为了优化汉语连续语音中HMM模型系统以提高识别性能,提出了分别为每个声母和韵母半音节声学模型选择最优的状态数的方法。通过综合考虑每个声母和韵母半音节声学模型在不同状态数下的段长均值、方差以及各自识别率这三者信息,作为进行最优模型状态数的选择准则。优化后的声学模型系统由状态数各不相同的声母半音节声学模型组成,同未优化前状态数统一的模型系统相比,音节识别性能提高了5.07个百分点。研究表明,每个声母和韵母半音节志学模型应根据情况选择不同的状态数,优化后的模型系统识别性能得到了提高。

In order to optimize the penformance of HMM-based Mandarin Continuous Speech recognition, the method of optimal selecting for each initial and final semi-syllable acoustic Hidden Markov Model state-number is proposed. It is proposed that to synthetically calculate three kinds of information, which are the duration mean, duration variance and correctness of each initial and final semi-syllable acoustic Hidden Markov Model, as the principle to select the optimal each semi-syllable acoustic Hidden Markov Model with different state-number and it shows the better performance of semi-syllable recognition by 5.07%, compared with the Hidden Markov Model system with the same statenumber. The research demonstrated that each initial and final semi-syllable acoustic Hidden Markov Model should be set up according to practicality and the recognition performance can be increased after the optimal selecting.