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CSL
2016
Springer

Articulatory feature based continuous speech recognition using probabilistic lexical modeling

3 years 6 months ago
Articulatory feature based continuous speech recognition using probabilistic lexical modeling
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 articulatory 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 ...
Ramya Rasipuram, Mathew Magimai-Doss
Added 01 Apr 2016
Updated 01 Apr 2016
Type Journal
Year 2016
Where CSL
Authors Ramya Rasipuram, Mathew Magimai-Doss
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