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Register a new userMulti-accent Speech Separation with One Shot Learning
فصل الكلام متعدد اللكنات مع التعلم بالرصاص واحد
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فصل الكلام هو مشكلة في مجال معالجة الكلام التي تمت دراستها على قدم وساق مؤخرا.ومع ذلك، لم يكن هناك الكثير من العمل في دراسة سيناريو لفصل الكلام متعدد اللكنات.أثارت مكبرات الصوت غير المرئية لهجات جديدة والضوضاء مشكلة عدم تطابق المجال والتي لا يمكن حلها بسهولة عن طريق أساليب التدريب المشتركة التقليدية.وبالتالي، طبقنا MAML و FOMAML لمعالجة هذه المشكلة وحصلت على أعلى قيم SI-SILRI أعلى من التدريب المشترك على جميع لهجات غير مرئية تقريبا.أثبت ذلك أن هاتين الطريقتين لديها القدرة على توليد معلمات مدربة جيدا للتكييف مع مخاليط الكلام من مكبرات الصوت الجديدة ولوجزات.علاوة على ذلك، اكتشفنا أن Fomaml يحصل على أداء مماثل مقارنة بالماما مع توفير الكثير من الوقت.
Speech separation is a problem in the field of speech processing that has been studied in full swing recently. However, there has not been much work studying a multi-accent speech separation scenario. Unseen speakers with new accents and noise aroused the domain mismatch problem which cannot be easily solved by conventional joint training methods. Thus, we applied MAML and FOMAML to tackle this problem and obtained higher average Si-SNRi values than joint training on almost all the unseen accents. This proved that these two methods do have the ability to generate well-trained parameters for adapting to speech mixtures of new speakers and accents. Furthermore, we found out that FOMAML obtains similar performance compared to MAML while saving a lot of time.
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