From HMM's to segment models: A unified view of stochastic modeling for speech recognition
From HMM's to segment models: A unified view of stochastic modeling for speech recognition
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DOI：
10.1109/89.536930
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发表时间：
1996-09-01
期刊：
影响因子：
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通讯作者：
Kimball, OA
中科院分区：
文献类型：
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作者：
Ostendorf, M;Digalakis, VV;Kimball, OA
近年来，已经提出了许多替代模型来解决隐马尔可夫模型（HMM）的一些缺点，隐马尔可夫模型是目前最流行的语音识别方法。特别地，已经描述了可以广泛地分类为分段模型的各种模型，用于表示语音识别应用中的观察向量的可变长度序列，由于这些方法之间有许多共同之处，包括普遍承认和培训问题，因此在一个统一的框架内考虑这些问题是有益的。因此，本文的目标将他描述一个一般的随机模型，其中包括在文献中提出的模型，指出模型的相似之处，在相关性和参数绑定假设，并绘制段模型和HMM之间的类比。此外，我们总结了实验结果评估不同的建模假设，并指出剩余的开放问题。
In recent years, many alternative models have been proposed to address some of the shortcomings of the hidden Markov model (HMM), which is currently the most popular approach to speech recognition, In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications, Since there are many aspects in common between these approaches, including the general recognition and training problems, it is useful to consider them in a unified framework. Thus, the goal of this paper will he to describe a general stochastic model that encompasses most of the models proposed in the literature, pointing out similarities of the models in terms of correlation and parameter tying assumptions, and drawing analogies between segment models and HMM's. In addition, we summarize experimental results assessing different modeling assumptions and point out remaining open questions.