中文摘要：

为了克服古典隐藏的 Markov 的缺点，当模特儿(唔) ， Markov 家庭模型(MFM ) ，一个新统计模型被建议。Markov 家庭模型被用于语音识别和自然语言处理。标注实验的独立地连续的语音识别实验和词类给那个 Markov 家庭模特儿看的说话者比隐藏的 Markov 模型有高效。精确在标注实验的词类从 94.642% ～ 96.214% 被提高，并且工作率被 11.9% 在语音识别实验减少关于唔基线系统。

英文摘要：

In order to overcome defects of the classical hidden Markov model （HMM）, Markov family model （MFM）, a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.