کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
558209 1451691 2016 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Articulatory feature based continuous speech recognition using probabilistic lexical modeling
ترجمه فارسی عنوان
تشخیص گفتار پیوسته مبتنی بر ویژگی شمرده شمرده با استفاده از مدل سازی واژگانی احتمالاتی
کلمات کلیدی
تشخیص گفتار خودکار؛ ویژگی های شمرده شمرده؛ مدل سازی واژگانی احتمالاتی؛
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• Approach for AF-based ASR in framework of probabilistic lexical modeling is proposed.
• Most approaches in literature use a knowledge-based deterministic phoneme-to-AF map.
• Approach incorporates a probabilistic phoneme-to-AF map learned through acoustic data.
• Analysis has shown that the approach allows different AFs to evolve asynchronously.
• Approach has potential to reduce word error rates by incorporating AFs in an ASR system.

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 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 36, March 2016, Pages 233–259
نویسندگان
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