CMUSphinx-Open Source Toolkit For Speech Recognition

最新推荐文章于 2025-08-15 22:59:14 发布

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最新推荐文章于 2025-08-15 22:59:14 发布

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文章标签：人工智能

原文链接：http://www.cnblogs.com/androidme/archive/2012/03/06/2381685.html

本文介绍了语音识别中使用的三大核心模型：声学模型、发音词典及语言模型，并详细解释了它们的功能与工作原理。声学模型负责捕捉每个音素的声学特性；发音词典建立词汇与音素之间的映射；语言模型则用于限制词汇搜索范围，预测可能的后续词汇，以提高识别准确性。

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Models

According to the speech structure, three models are used in speech recognition to do the match:

An acoustic model contains acoustic properties for each senone. There are context-independent models that contain properties (most probable feature vectors for each phone) and context-dependent ones (built from senones with context).

A phonetic dictionary contains a mapping from words to phones. This mapping is not very effective. For example, only two to three pronunciation variants are noted in it, but it's practical enough most of the time. The dictionary is not the only variant of mapper from words to phones. It could be done with some complex function learned with a machine learning algorithm.

A language model is used to restrict word search. It defines which word could follow previously recognized words (remember that matching is a sequential process) and helps to significantly restrict the matching process by stripping words that are not probable. Most common language models used are n-gram language models-these contain statistics of word sequences-and finite state language models-these define speech sequences by finite state automation, sometimes with weights. To reach a good accuracy rate, your language model must be very successful in search space restriction. This means it should be very good at predicting the next word. A language model usually restricts the vocabulary considered to the words it contains. That's an issue for name recognition. To deal with this, a language model can contain smaller chunks like subwords or even phones. Please note that search space restriction in this case is usually worse and corresponding recognition accuracies are lower than with a word-based language model.

Those three entities are combined together in an engine to recognize speech. If you are going to apply your engine for some other language, you need to get such structures in place. For many languages there are acoustic models, phonetic dictionaries and even large vocabulary language models available for download.

转载于:https://www.cnblogs.com/androidme/archive/2012/03/06/2381685.html