：为了使应力变异在顽健语音识别系统中能够达到较好的识别效果，研究了基于隐马

尔可夫模型(HMM)的自适应技术，提出了将最大后验概率(MAP)和最大似然回归方法(MLLR)用

于应力变异语音的自适应中。实验结果表明，与基本系统相比，两种方法均有效地提高系统识别

率。以SD为初始模型的最大后验概率方法在150个训练样本时识别效果最好，可以达到90．4％ 。-: In order to stress variation in the robustness of speech recognition system can achieve better recognition results, based on Hidden Markov Model (HMM) of adaptive technology, put forward a maximum a posteriori probability (MAP) and Maximum Likelihood regression (MLLR) for the stress of the adaptive variation in voice. The experimental results show that compared with the basic system, both methods are effective to improve the system recognition rate. SD as the initial model to the maximum a posteriori probability method in 150 training samples to identify the best, can reach 90.4 .