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Zoughi T, Homayounpour M M. Adaptive Windows Convolutional Neural Network for Speech Recognition. JSDP 2018; 15 (3) :13-30
URL: http://jsdp.rcisp.ac.ir/article-1-706-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-706-fa.html
ذوقی تکتم، همایون پور محمد مهدی. شبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار. پردازش علائم و داده‌ها. 1397; 15 (3) :13-30
دانشگاه صنعتی امیرکبیر
چکیده: (4537 مشاهده)
در حالی‌که سامانه‌های بازشناسی گفتار به‌طور پیوسته در حال ارتقا می‌باشند و شاهد استفاده گسترده از آن‌ها می‌باشیم، اما دقت این سامانه‌ها فاصله زیادی نسبت به توان بازشناسی انسان دارد و در شرایط ناسازگار این فاصله افزایش می‌یابد. یکی از علل اصلی این مسئله تغییرات زیاد سیگنال گفتار است. در سال‌های اخیر، استفاده از شبکههای عصبی عمیق در ترکیب با مدل مخفی مارکف، موفقیت‌های قابل توجهی در حوزه پردازش گفتار داشته است. این مقاله به‌دنبال مدل‌کردن بهتر گفتار با استفاده از تغییر ساختار در شبکه عصبی پیچشی عمیق است؛ به‌نحوی که با تنوعاتِ بیان گویندگان در سیگنال گفتار منطبق‌تر شود. در این راه، مدل‌های موجود و انجام استنتاج بر روی آن‌ها را بهبود و گسترش خواهیم داد. در این مقاله با ارائه شبکه پیچشی عمیق با پنجرههای قابل تطبیق سامانه بازشناسی گفتار را نسبت به تفاوت بیان در بین گویندگان و تفاوت در بیان‌های یک گوینده مقاوم خواهیم کرد. تحلیلها و نتایج آزمایش‌های صورت‌گرفته بر روی دادگان گفتار فارسدات و TIMIT نشان داد که روش پیشنهادی خطای مطلق بازشناسی واج را نسبت به شبکه پیچشی عمیق بهترتیب به میزان 2/1 و 1/1 درصد کاهش می‌دهد که این مقدار در مسئله بازشناسی گفتار مقدار قابل توجهی است.
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات پردازش گفتار
دریافت: 1397/1/15 | پذیرش: 1397/9/26 | انتشار: 1397/9/28 | انتشار الکترونیک: 1397/9/28
دریافت: 1397/1/15 | پذیرش: 1397/9/26 | انتشار: 1397/9/28 | انتشار الکترونیک: 1397/9/28
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