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Improved pronunciation prediction accuracy using morphology

تحسين دقة التنبؤ النطق باستخدام المورفولوجيا

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-specific paradigms. In this work we explore how deep recurrent neural networks can be used to automatically learn and exploit this pattern to improve the pronunciation prediction quality of words related by morphological inflection. We propose two novel approaches for supplying morphological information, using the word's morphological class and its lemma, which are typically annotated in standard lexicons. We report improvements across a number of European languages with varying degrees of phonological and morphological complexity, and two language families, with greater improvements for languages where the pronunciation prediction task is inherently more challenging. We also observe that combining bidirectional LSTM networks with attention mechanisms is an effective neural approach for the computational problem considered, across languages. Our approach seems particularly beneficial in the low resource setting, both by itself and in conjunction with transfer learning.



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أهداف البحث: -1 دراسة نظرية عن أهمية و أثر الدقة في التنبؤ بالمبيعات على خطط الإنتاج و التسويق و التوزيع. -2 دراسة مرجعية عن التنقيب في البيانات و التنبؤ باستخدام السلاسل الزمنية و الشبكات العصبونية. -3 استخدام الشبكات العصبية الصناعية في زيادة د قة التنبؤ بحجم المبيعات الشهرية لشركة الفنار. -4 اختبار تفوق الشبكات العصبية في التنبؤ على نموذجي المتوسطات المتحركة و الانحدار.

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