ارائه دو روش داده‌افزایی برای بازشناسی گفتار با دادگان محدود: پوشاندن تدریجی و پوشاندن آگاه از فراوانی کلمات

ملخص المقالة :

کمبود داده، چالش اصلی بازشناسی گفتار مبتنی بر شبکه‌های عصبی عمیق است و داده‌افزایی یک راه‌حل مؤثر برای این مسئله می‌باشد. این مقاله ضمن ارائه طبقه‌بندی جامع روش‌های داده‌افزایی در بازشناسی گفتار، به بررسی اثربخشی مهم‌ترین روش‌های این حوزه یعنی روش‌های مبتنی بر پوشاندن در شرایط محدودیت دادگان می‌پردازد. روش‌های مورد بررسی دو روش قدرتمند SpecAugment و پوشاندن کلمه هستند. این روش‌ها علی‌رغم کارایی اثبات‌شده در شرایط دادگان فراوان، در شرایط دادگان محدود، کمتر مطالعه شده‌اند. در تحقیق حاضر، پس از تحلیل معایب روش پوشاندن کلمه در شرایط دادگان محدود، دو روش نوآورانه برای رفع این ایرادات ارائه می‌دهیم: (1) پوشاندن تدریجی که آموزش را با پوشاندن در سطح فریم آغاز و سپس به پوشاندن کلمه تغییر می‌دهد؛ (2) پوشاندن آگاه از فراوانی کلمات که ابتدا کلمات پرتکرار و سپس کلمات کم‌تکرار پوشانده می‌شوند. آزمایشات روی مجموعه 100 ساعتی پیکره LibriSpeech نشان می‌دهد روش پیشنهادی اول به WER 8_/6% در مجموعه تمیز و 2_/18% در مجموعه چالش‌برانگیز رسیده که به‌ترتیب 8_/6% و 2_/4% بهبود نسبت به روش رقابتی SpecAugment حاصل کرده است. روش پیشنهادی دوم نیز به WER 6_/6% در مجموعه تمیز و 3_/17% در مجموعه چالش‌برانگیز رسیده که به‌ترتیب 6_/9% و 9_/8% بهبود نسبت به SpecAugment کسب کرده است.

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