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Register a new userBTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text
BTS: النسخ الخلفي للمعالج إلى النص إلى النص باستخدام النص إلى كلام إلى النص
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مع وجود شعبية متزايدة للمتحدثين الذكية، مثل الأمازون اليكسا، أصبح الكلام أحد أهم طرق التفاعل بين الإنسان والحاسوب. يمكن القول إن التعرف التلقائي على التعرف على الكلام (ASR) هو العنصر الأكثر أهمية في هذه الأنظمة، حيث ينتشر أخطاء في التعرف على الكلام إلى مكونات المصب التي تتحلل بشكل كبير من تجربة المستخدم. طريقة بسيطة وفعالة لتحسين دقة التعرف على الكلام هي تطبيق ما بعد المعالج التلقائي نتيجة التعرف. ومع ذلك، فإن التدريب على معالج ما بعد البيع يتطلب شركة موازية تم إنشاؤها بواسطة Annwotators البشرية، وهي مكلفة وغير قابلة للتحجيم. لتخفيف هذه المشكلة، نقترح النسخ الخلفي (BTS)، وهي طريقة قائمة على الدنيوية التي يمكن أن تنشئ مثل هذه الشركة دون عمل بشري. باستخدام CORPUS RAW، يقوم BTS بتلف النص باستخدام أنظمة تحويل النص إلى كلام (TTS) ونص الكلام إلى النص (STT). بعد ذلك، يمكن تدريب نموذج ما بعد المعالجة على إعادة بناء النص الأصلي مع إعطاء المدخلات التالفة. تبين التقييمات الكمية والنوعية أن المعالج بعد المعالج المدرب باستخدام نهجنا فعال للغاية في إصلاح أخطاء التعرف على الكلام غير تافهة مثل سوء الكلمات الأجنبية. نقدم Corpus الموازي الذي تم إنشاؤه ومنصة ما بعد المعالجة لجعل نتائجنا متاحة للجمهور.
With the growing popularity of smart speakers, such as Amazon Alexa, speech is becoming one of the most important modes of human-computer interaction. Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience. A simple and effective way to improve the speech recognition accuracy is to apply automatic post-processor to the recognition result. However, training a post-processor requires parallel corpora created by human annotators, which are expensive and not scalable. To alleviate this problem, we propose Back TranScription (BTS), a denoising-based method that can create such corpora without human labor. Using a raw corpus, BTS corrupts the text using Text-to-Speech (TTS) and Speech-to-Text (STT) systems. Then, a post-processing model can be trained to reconstruct the original text given the corrupted input. Quantitative and qualitative evaluations show that a post-processor trained using our approach is highly effective in fixing non-trivial speech recognition errors such as mishandling foreign words. We present the generated parallel corpus and post-processing platform to make our results publicly available.
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