语音识别中的模糊隐马尔科夫模型新方法

Proceedings of the IEEE Symposium on Emerging Technologies, 2005. Pub Date : 2005-12-19 DOI:10.1109/ICET.2005.1558851

M. Tarihi, M. Taheri, H. Bababeyk

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引用次数: 4

摘要

本文针对隐马尔可夫模型（HMM）提出了一种 Ftuzzi 方法。这种方法被称为 "模糊 HMM"，它是期望最大化算法在 HMM 中的应用，用于语音和说话人识别。本文解释了基于这两种算法的离散和连续 HMM 参数估计方程和方法，并对两种语音识别方法的性能进行了分析。与传统的 HMM 相比，本文展示了模糊 HMVM 更好的结果。

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A new method for fuzzy hidden Markov models in speech recognition

Thispaperproposes a Ftuzzi approach tothe Hidden Markov Model(HMM). This method called the fizzy HMMfbrspeech andspeaker recognition asan application offizzy expectation maximizing algorithm inHMM.Thefitzzy HMM algorithm isregar-ded asan application of'the,fizzy expectation-maximization (EM)algorithm totheBatum-Welch algorithm inthe HMM. TheTexas Instruments p4uised speech and speaker recognition experiments andshowbetter results fbr.fiuzzv HMMscompared withconventional HMMs.Equation andhowestimation ofdiscrete and continuious HMM parameters on basedthistwo algorithm isexplained andperfbrmance oftwospeech recognition methodtbronehundred issurveved. This papershowbetter results forthefiuzzy HMVM, compared with theconventional HMM.

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Proceedings of the IEEE Symposium on Emerging Technologies, 2005.

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