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بهبود نرخ تشخیص احساس از روی گفتار با استفاده از تفکیک جنسیتی | ||
| مدل سازی در مهندسی | ||
| مقاله 15، دوره 15، شماره 48، خرداد 1396، صفحه 183-200 اصل مقاله (1.35 M) | ||
| نوع مقاله: کاربردی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2017.2444 | ||
| نویسندگان | ||
| علی حریمی* 1؛ خشایار یغمائی2 | ||
| 1دانشگاه آزاد اسلامی واحد شاهرود | ||
| 2دانشگاه سمنان | ||
| تاریخ دریافت: 14 شهریور 1391، تاریخ بازنگری: 27 اردیبهشت 1393، تاریخ پذیرش: 23 آذر 1394 | ||
| چکیده | ||
| تشخیص احساس از روی سیگنال گفتار یکی از شاخه‌های نسبتاً جدید در پردازش گفتار می‌باشد که می‌تواند در تعامل انسان و روبات نقش مهمی ایفا کند. در این مقاله ضمن استفاده از دو نوع ویژگی طیفی جدید به منظور افزایش نرخ بازشناسی به بررسی تاثیر جنسیت گویندگان در تشخیص احساس پرداخته شده است. ویژگی‌های یاد شده با استفاده از روش‌های پردازش تصویر، از تصویر طیف‌نگاره سیگنال گفتار استخراج می‌شوند . در این تحقیق به منظور جداسازی احساس‌های مختلف از یکدیگر از طبقه‌بند مرتبه ای استفاده شده است. به منظور بهینه سازی ساختار این طبقه‌بند، ابتدا جداپذیر ترین کلاس ها از هم جدا می‌شوند تا خطای ایجاد شده در مراحل اولیه طبقه‌بندی حداقل بوده و این خطا در الگوریتم منتشر نشود. سیستم پیشنهادی بر روی پایگاه داده‌ی آلمانی برلین آزمایش شده است. بر اساس نتایج بدست آمده نرخ تشخیص برای گویندگان مختلط 4/43% می‌باشد که این مقدار پس از تفکیک گویندگان بر اساس جنسیت به 86/82% افزایش پیدا می‌کند. نرخ تشخیص برای گویندگان زن 05/83% و برای مردان 61/82% بدست آمده است. | ||
| کلیدواژه‌ها | ||
| تشخیص احساس؛ احساس در زنان و مردان؛ الگوهای طیفی؛ ویژگی‌های هارمونیکی | ||
| عنوان مقاله [English] | ||
| improving speech emotion recognition via gender classification | ||
| نویسندگان [English] | ||
| Ali Harimi1؛ Khashayar Yaghmaie2 | ||
| چکیده [English] | ||
| Speech emotion recognition is a relatively new field of research that could plays an important role in man-machine interaction. In this paper we use from two new spectral features for the automatic recognition of human affective information from speech. These features are extracted from the spectrogram of speech signal by image processing techniques. Also we study the effects of gender information on speech emotion recognition. Hierarchical SVM base classifiers are designed to classify speech signals according to their emotional states. Classifiers are optimized by the Fisher Discriminant Ratio (FDR) to classify the most separable classes at the upper nodes, which can reduce the classification error. The proposed algorithm tested on the well known Berlin database for the male and female speakers separately and in combination. The overall recognition rate of 43.4% is obtained for the coeducational speakers. The results show the 39.46% improvement when the gender information is used. | ||
| کلیدواژه‌ها [English] | ||
| emotion recognition, speech processing, emotion in males and females, spectral patterns, harmonic energy features | ||
| مراجع | ||
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