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Register a new userIncorporating speaker embedding and post-filter network for improving speaker similarity of personalized speech synthesis system
دمج متكلم التضمين وشبكة ما بعد المرشح لتحسين تشابه المتكلم لنظام توليف الكلام المخصص
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في السنوات الأخيرة، يمكن لنظام توليف الكلام إنشاء خطاب بجودة الكلام العالية. ومع ذلك، لا يزال نظام النص إلى كلام متعدد الكلام (TTS) يتطلب كمية كبيرة من بيانات الكلام لكل مكبر صوت مستهدف. في هذه الدراسة، نود إنشاء نظام TTS متعدد المتكلم من خلال دمج وحدات فرعية في نظام توليف الكلام المصنوع من الشبكة العصبية المصطنعة لتخفيف هذه المشكلة. تتمثل الوحدة الأولى في إضافة مكبر صوت إلى وحدة الترميز لتوليد الكلام في حين أن كمية كبيرة من بيانات الكلام من السماعة المستهدفة ليست ضرورية. بالنسبة لطريقة تضمين المتكلم، في دراستنا، يتم مقارنة طريقتان رئيسيتان لضمان المتكلم، وهي تضمين مكبر الصوت التضمين وإدماج تحويل الصوت، بتحديد ما هو مناسب لنظام TTS الشخصي الخاص بنا. ثانيا، استبدلنا الوحدة النمطية الإضافية التقليدية، التي تم تبنيها لتعزيز تسلسل طيف الإخراج، لزيادة تحسين جودة خطاب الكلام الذي تم إنشاؤه. هنا، يتم استخدام شبكة ما بعد المرشح. أخيرا، أظهرت نتائج التجربة أن تضمين المتكلم مفيد من خلال إضافةه إلى وحدة ترميز ونطق الكلام الناتج ينظر بالفعل إلى السماعة المستهدفة. أيضا، شبكة ما بعد التصفية ليست فقط تحسين جودة الكلام وتعزز أيضا تشابه المتكلم من كلام الكلام الناتج. يمكن لنظام TTS المصمم أن يولد كلام الكلام للمتكلم المستهدف في أقل من 2 ثانية. في المستقبل، نود مزيد من التحقيق في مكافحة قابلية التحكم في معدل التحدث أو حالة المشاعر المتصورة للكلمة التي تم إنشاؤها.
In recent years, speech synthesis system can generate speech with high speech quality. However, multi-speaker text-to-speech (TTS) system still require large amount of speech data for each target speaker. In this study, we would like to construct a multi-speaker TTS system by incorporating two sub modules into artificial neural network-based speech synthesis system to alleviate this problem. First module is to add speaker embedding into encoding module for generating speech while a large amount of the speech data from target speaker is not necessary. For speaker embedding method, in our study, two main speaker embedding methods, namely speaker verification embedding and voice conversion embedding, are compared to deciding which one is suitable for our personalized TTS system. Second, we substituted the conventional post-net module, which is adopted to enhance the output spectrum sequence, to further improving the speech quality of the generated speech utterance. Here, a post-filter network is used. Finally, experiment results showed that the speaker embedding is useful by adding it into encoding module and the resultant speech utterance indeed perceived as the target speaker. Also, the post-filter network not only improving the speech quality and also enhancing the speaker similarity of the generated speech utterances. The constructed TTS system can generate a speech utterance of the target speaker in fewer than 2 seconds. In the future, we would like to further investigate the controllability of the speaking rate or perceived emotion state of the generated speech.
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