استفاده از مدل‌های وابسته به محتوا در واژه‌ياب گفتار متمايزساز
الموضوعات :
شیما طبیبیان
1
(پژوهشگاه هوافضا)
احمد اکبری
2
(دانشگاه علم و صنعت ایران)
بابک ناصرشريف
3
(دانشگاه صنعتی خواجه نصیرالدین طوسی)
الکلمات المفتاحية: استخراج ويژگي بازشناس واج مستقل از محتوا وابسته به محتوا ماشين بردار پشتيبان واژه‌يابي گفتار متمايزساز,
ملخص المقالة :
رويكردهاي واژه‌يابي گفتار به دو گروه تقسيم می‌شوند: رويكردهاي مبتني بر مدل مخفي ماركف و رويكردهاي متمايزساز. يكي از فوايد رويكردهاي مبتني بر مدل مخفي ماركف، قابليت استفاده از اطلاعات وابسته به محتوا (سه واج) در جهت بهبود كارايي سيستم واژه‌ياب گفتار مي‌باشد. از طرفی، عدم امكان استفاده از اطلاعات وابسته به محتوا يكي از معایب رويكردهاي واژه‌يابي گفتار متمايزساز محسوب مي‌شود. در اين مقاله، راهكاري براي رفع اين عیب ارائه شده که به اين منظور، بخش استخراج ويژگي يك سيستم واژه‌ياب گفتار متمايزساز مبتنی بر الگوریتم تکاملی (EDSTD)- كه در كارهاي قبلي ما ارائه شده است- به گونه‌اي تغيير یافته كه اطلاعات وابسته به محتوا را در نظر بگيرد. در مرحله نخست، يك رويكرد استخراج ويژگي مستقل از محتوا پيشنهاد شده و سپس رويكردي براي به كارگيري اطلاعات وابسته به محتوا در بخش استخراج ويژگي ارائه شده است. نتايج ارزيابي‌ها روی دادگان TIMIT حاكي از آن است كه نرخ بازشناسي سيستم EDSTD وابسته به محتوا (CD-EDSTD) در اخطار اشتباه بر كلمه كليدي بر ساعت بزرگ‌تر از دو، حدود 3% از نرخ بازشناسي درست سيستم EDSTD مستقل از محتوا (CI-EDSTD) بالاتر است. هزينه اين بهبود دقت، حدود 36/0 افت سرعت پاسخ‌گويي است كه قابل چشم‌پوشي مي‌باشد.
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