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صفحه اصلی
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سی و یکمین کنفرانس بین المللی مهندسی برق
Speech Emotion Recognition Using Transfer Learning and Self-Supervised Speech Representation Learning
نویسندگان :
Marziye Azad
1
Babak Nasersharif
2
1- دانشگاه صنعتی خواجه نصیرالدین طوسی
2- دانشگاه صنعتی خواجه نصیرالدین طوسی
کلمات کلیدی :
HuBERT،Conformer،self-supervised learning،speech emotion recognition
چکیده :
Self-supervised speech representation learning (S3RL) models like wav2vec2.0, Hidden-unit BERT (HuBERT), and WavLM are trained with a great amount of speech data and subsequently give a general purpose speech representation that then needs to be finetuned for different speech processing tasks like ASR. Despite these models’ good performance, they suffer from massive structures and a great number of parameters which makes their finetuning inapplicable for low-resource tasks like speech emotion recognition. In this paper, a small model is proposed for speech emotion recognition based on the Hubert model by transferring the Hubert convolutional feature encoder and substituting all of its transformers with a simple conformer block. Then this simple model is trained with emotional speech signals. The experimental results demonstrate that the proposed model has comparable results with other well-performing S3RL models.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
