مقاوم‌سازی بازشناسی صحبت با به کارگیری فیلتر غیر خطی نامتقارن و استفاده از ویژگی‌های طیفی دلتا
محورهای موضوعی : مهندسی برق و کامپیوتر
حسن فرسی
1
,
سمانه کوهی مقدم
2
1 - دانشگاه بیرجند
2 - دانشگاه پیام‌نور واحد مشهد
کلید واژه: بازشناسی صحبت ضرایب کپسترال نرمالیزه‌شده توان فیلتر غیر خطی نامتقارن ویژگی‌های کپسترال دلتا,
چکیده مقاله :
در این مقاله یک الگوریتم استخراج ویژگی مقاوم به نویز را پیشنهاد می‌دهیم. در این الگوریتم به منظور استخراج ویژگی از یک فیلتر غیر خطی و پوشش موقتی استفاده می‌شود و با بهره‌گیری ازویژگی دلتا- طیفی به جای ویژگی کپسترال- دلتا دقت بازشناسی صحبت به طور مطلوبی افزایش می‌یابد. تقریباً همه سیستم‌های خودکار تشخیص صحبت (ASR) کنونی از ویژگی‌های کپسترال- دلتا و دلتا- دلتا برای استخراج ویژگی صحبت استفاده می‌کنند. در این مقاله هدف، رسیدن به ویژگی‌های مقاومی است که در شرایط مختلف نویزی بهبود بیشتری برای بازشناسی صحبت فراهم می‌آورد. برای تحقق این امر بر روی برخی از مشخصات کلیدی صحبت (خصوصاً مشخصات غیر ایستان صحبت) متمرکز شده که با سیگنال‌های نویزی اختلاف دارد. نتایج آزمایش‌های انجام‌گرفته نشان می‌دهد که دقت بازشناسی در مقایسه با MFCC و PLP در حضور انواع مختلف نویز بهبود یافته است.
In this paper, we propose a new feature extraction algorithm which is robust against noise. In the proposed algorithm, a non-linear filter with temporal masking are used for speech feature extraction and by applying delta spectral characteristics instead of delta cepstral, the accuracy of speech recognition is improved. Almost, all present Automatic Speech Recognition (ASR) systems use cepstral-delta and delta-delta characteristics for speech feature extraction. The aim of this paper is to reach the robust speech features which provide more accurate speech recognition under different noisy conditions. This is achieved by focusing on speech key features (especially non-stationary speech features) which highly differ from the noise signals. The obtaining experimental results show that the accuracy of speech recognition improves in comparison with traditional methods such as PLP and MFCC.
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