Speech emotion recognition (SER) has garnered increasing attention due to its
wide range of applications in various fields, including human-machine
interaction, virtual assistants, and mental health assistance. However,
existing SER methods often overlook the information gap between the
pre-training speech recognition task and the downstream SER task, resulting in
sub-optimal performance. Moreover, current methods require much time for
fine-tuning on each specific speech dataset, such as IEMOCAP, which limits
their effectiveness in real-world scenarios with large-scale noisy data. To
address these issues, we propose an active learning (AL)-based fine-tuning
framework for SER, called \textsc{After}, 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 speech recognition task and the downstream speech emotion
recognition task. Then, AL methods are employed to iteratively select a subset
of the most informative and diverse samples for fine-tuning, thereby reducing
time consumption. Experiments demonstrate that our proposed method
\textsc{After}, using only 20\% of samples, improves accuracy by 8.45\% and
reduces time consumption by 79\%. The additional extension of \textsc{After}
and ablation studies further confirm its effectiveness and applicability to
various real-world scenarios. Our source code is available on Github for
reproducibility. (this https URL).

通过利用主动学习 (AL) 和任务适应预训练 (TAPT) 方法，我们提出了一种名为	extsc {After} 的情感语音识别 (SER) 框架，用于增强性能和效率，实验证明我们的方法在仅使用 20％的样本时，准确率提高了 8.45％，时间消耗减少了 79％。

使用任务自适应预训练的主动学习方法用于语音情感识别

Active Learning with Task Adaptation Pre-training for Speech Emotion  Recognition

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.

提出了一种基于主动学习和微调的语音情感识别框架，使用任务适应性预训练和主动学习方法来提高性能和效率，并通过仅使用 20% 的样本来改善 8.45% 的准确性和减少 79% 的时间消耗。