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Register a new userLanguage ID Prediction from Speech Using Self-Attentive Pooling
تنبؤ معرف اللغة من الكلام باستخدام تجمع اليقظة الذاتية
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توضح هذه المذكرة إرسال NTR-TSU المهمة المشتركة SIGTYP 2021 بشأن التنبؤ معرفات اللغة من الكلام.تعد تحديد اللغة المنطوقة (غطاء) خطوة مهمة في خط أنابيب نظام التعرف الآلي متعدد اللغات (ASR).بالنسبة للعديد من اللغات المنخفضة واللغات المهددة بالانقراض، قد تكون التسجيلات المفردة المفردة فقط متاحة، مطالبة بالحاجة إلى أنظمة معرف اللغة للمجال ومكبر الصوت.في هذه المذكرة، نوضح أن الشبكة العصبية التنافسية مع طبقة تجمع ذاتي اليقظة تظهر نتائج واعدة لمهمة تحديد اللغة.
This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be available, demanding a need for domain and speaker-invariant language ID systems. In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results for the language identification task.
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This paper describes the model built for the SIGTYP 2021 Shared Task aimed at identifying 18 typologically different languages from speech recordings. Mel-frequency cepstral coefficients derived from audio files are transformed into spectrograms, whi
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