该文研究了基于数据模拟方法和HMM（隐马尔科夫模型）自适应的电话信道条件下语音识别问题。
This paper addresses the problem of speech recognition under telephone channel conditions using data simulation method and HMM(Hidden Markov Model)adaptation.
识别试验利用模拟电话语音评估了HMM识别器做MLLR自适应前后的性能。
MLLR adaptations are conducted to evaluate the performances of the HMM recognizers, which are trained from the clean speech and generated data respectively.
分析了在航管雷达模拟系统增加语音识别和合成功能的实现过程。
This paper analyses the process of realization about speech recognition and speech synthesization in digital radar simulated system.
计算机模拟实验显示，经该方法预处理后的语音送入隐马尔柯夫模型的语音识别系统比传统算法有更高的识别率。
Traditional training methods for hidden Markov models, such as Baum Welch method can only lead to local optima, which reduces the recognition rate.
计算机模拟实验显示，经该方法预处理后的语音送入隐马尔柯夫模型的语音识别系统比传统算法有更高的识别率。
Traditional training methods for hidden Markov models, such as Baum Welch method can only lead to local optima, which reduces the recognition rate.
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