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Articulatory feature based continuous speech recognition using probabilistic lexical modeling

机译:基于发音特征的概率词汇建模的连续语音识别

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Phonological studies suggest that the typical subword units such as phones or phonemes used in automatic speech recognition systems can be decomposed into a set of features based on the articulators used to produce the sound. Most of the current approaches to integrate aiticulatory feature (AF) representations into an automatic speech recognition (ASR) system are based on a deterministic knowledge-based phoneme-to-AF relationship. In this paper, we propose a novel two stage approach in the framework of probabilistic lexical modeling to integrate AF representations into an ASR system. In the first stage, the relationship between acoustic feature observations and various AFs is modeled. In the second stage, a probabilistic relationship between subword units and AFs is learned using transcribed speech data. Our studies on a continuous speech recognition task show that the proposed approach effectively integrates AFs into an ASR system. Furthermore, the studies show that either phonemes or graphemes can be used as subword units. Analysis of the probabilistic relationship captured by the parameters has shown that the approach is capable of adapting the knowledge-based phoneme-to-AF representations using speech data; and allows different AFs to evolve asynchronously.
机译:语音研究表明,基于用于产生声音的发音器,可以将典型的子词单元(例如自动语音识别系统中使用的电话或音素)分解为一组功能。当前,大多数将自动特征(AF)表示集成到自动语音识别(ASR)系统中的方法都是基于基于确定性知识的音素与AF的关系。在本文中,我们提出了一种在概率词法建模框架中将AF表示集成到ASR系统中的新颖的两阶段方法。在第一阶段,对声学特征观测和各种自动对焦之间的关系进行建模。在第二阶段,使用转录的语音数据学习子词单元和AF之间的概率关系。我们对连续语音识别任务的研究表明,所提出的方法有效地将AF集成到了ASR系统中。此外,研究表明音素或字素都可以用作子词单位。对由参数捕获的概率关系的分析表明,该方法能够使用语音数据来适应基于知识的音素到AF表示。并允许不同的AF异步发展。

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