کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946668 1439411 2017 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluating deep learning architectures for Speech Emotion Recognition
ترجمه فارسی عنوان
ارزیابی معماری یادگیری عمیق برای تشخیص گفتار احساسی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models' performances.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 92, August 2017, Pages 60-68
نویسندگان
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