کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
565904 1452039 2014 12 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
Phonetic feature extraction for context-sensitive glottal source processing
ترجمه فارسی عنوان
استخراج ویژگی فونتیک برای پردازش منبع حساس به متن
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• Comparison of classifiers for phonetic feature extraction.
• Evaluation on a large body of data involving substantial phonation type variation.
• Exploitation of detected phonetic context for deriving glottal source feature data.
• Glottal source feature data in syllabic regions most effective for classifying voice quality.

The effectiveness of glottal source analysis is known to be dependent on the phonetic properties of its concomitant supraglottal features. Phonetic classes like nasals and fricatives are particularly problematic. Their acoustic characteristics, including zeros in the vocal tract spectrum and aperiodic noise, can have a negative effect on glottal inverse filtering, a necessary pre-requisite to glottal source analysis. In this paper, we first describe and evaluate a set of binary feature extractors, for phonetic classes with relevance for glottal source analysis. As voice quality classification is typically achieved using feature data derived by glottal source analysis, we then investigate the effect of removing data from certain detected phonetic regions on the classification accuracy. For the phonetic feature extraction, classification algorithms based on Artificial Neural Networks (ANNs), Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs) are compared. Experiments demonstrate that the discriminative classifiers (i.e. ANNs and SVMs) in general give better results compared with the generative learning algorithm (i.e. GMMs). This accuracy generally decreases according to the sparseness of the feature (e.g., accuracy is lower for nasals compared to syllabic regions). We find best classification of voice quality when just using glottal source parameter data derived within detected syllabic regions.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Speech Communication - Volume 59, April 2014, Pages 10–21
نویسندگان
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