SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929546650976256 |
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| author | Jain, Sarthak Phukan, Orchid Chetia Behera, Swarup Ranjan Buduru, Arun Balaji Sharma, Rajesh |
| author_facet | Jain, Sarthak Phukan, Orchid Chetia Behera, Swarup Ranjan Buduru, Arun Balaji Sharma, Rajesh |
| contents | In this work, we introduce SeQuiFi, a novel approach for mitigating catastrophic forgetting (CF) in speech emotion recognition (SER). SeQuiFi adopts a sequential class-finetuning strategy, where the model is fine-tuned incrementally on one emotion class at a time, preserving and enhancing retention for each class. While various state-of-the-art (SOTA) methods, such as regularization-based, memory-based, and weight-averaging techniques, have been proposed to address CF, it still remains a challenge, particularly with diverse and multilingual datasets. Through extensive experiments, we demonstrate that SeQuiFi significantly outperforms both vanilla fine-tuning and SOTA continual learning techniques in terms of accuracy and F1 scores on multiple benchmark SER datasets, including CREMA-D, RAVDESS, Emo-DB, MESD, and SHEMO, covering different languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning Jain, Sarthak Phukan, Orchid Chetia Behera, Swarup Ranjan Buduru, Arun Balaji Sharma, Rajesh Audio and Speech Processing Sound 68T45 I.2.7 In this work, we introduce SeQuiFi, a novel approach for mitigating catastrophic forgetting (CF) in speech emotion recognition (SER). SeQuiFi adopts a sequential class-finetuning strategy, where the model is fine-tuned incrementally on one emotion class at a time, preserving and enhancing retention for each class. While various state-of-the-art (SOTA) methods, such as regularization-based, memory-based, and weight-averaging techniques, have been proposed to address CF, it still remains a challenge, particularly with diverse and multilingual datasets. Through extensive experiments, we demonstrate that SeQuiFi significantly outperforms both vanilla fine-tuning and SOTA continual learning techniques in terms of accuracy and F1 scores on multiple benchmark SER datasets, including CREMA-D, RAVDESS, Emo-DB, MESD, and SHEMO, covering different languages. |
| title | SeQuiFi: Mitigating Catastrophic Forgetting in Speech Emotion Recognition with Sequential Class-Finetuning |
| topic | Audio and Speech Processing Sound 68T45 I.2.7 |
| url | https://arxiv.org/abs/2410.12567 |