Emotion information recovery potential of wav2vec2 network fine-tuned for speech recognition task
| Type of publication: | Conference paper |
| Citation: | Purohit_ICASSP-2_2025 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Year: | 2025 |
| Month: | April |
| Publisher: | IEEE |
| Location: | Hyderabad, India |
| Abstract: | Fine-tuning has become a norm to achieve state-of-the-art performance when employing pre-trained networks like foundation models. These models are typically pre-trained on large-scale unannotated data using self-supervised learning (SSL) methods. The SSL-based pre-training on large-scale data enables the network to learn the inherent structure/properties of the data, providing it with capabilities in generalization and knowledge transfer for various downstream tasks. However, when fine-tuned for a specific task, these models become task-specific. Finetuning may cause distortions in the patterns learned by the network during pre-training. In this work, we investigate these distortions by analyzing the network's information recovery capabilities by designing a study where speech emotion recognition is the target task and automatic speech recognition is an intermediary task. We show that the network recovers the task-specific information but with a shift in the decisions also through attention analysis, we demonstrate some layers do not recover the information fully. |
| Keywords: | ASR, domain adaptation, Finetuning, Foundation Models, Speech Emotion Recognition, wav2vec2.0 |
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EMIL |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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