Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2310.00283v1
• Date: Sat, 30 Sep 2023 07:23:29 GMT
• Title: Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition
• Authors: Dongyuan Li, Yusong Wang, Kotaro Funakoshi, Manabu Okumura
• Abstract summary: Speech emotion recognition (SER) has drawn increasing attention for its applications in human-machine interaction. Existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance. We propose an active learning (AL) based Fine-Tuning framework for SER that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency.
• Score: 20.28850074164053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech emotion recognition (SER) has drawn increasing attention for its applications in human-machine interaction. However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance. Moreover, they require much time to fine-tune on each specific speech dataset, restricting their effectiveness in real-world scenes with large-scale noisy data. To address these issues, we propose an active learning (AL) based Fine-Tuning framework for SER that leverages task adaptation pre-training (TAPT) and AL methods to enhance performance and efficiency. Specifically, we first use TAPT to minimize the information gap between the pre-training and the downstream task. Then, AL methods are used to iteratively select a subset of the most informative and diverse samples for fine-tuning, reducing time consumption. Experiments demonstrate that using only 20\%pt. samples improves 8.45\%pt. accuracy and reduces 79\%pt. time consumption.

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