走向强大的家庭-婴儿音频分析,基于Wav2vec 2.0在大型未标记的家庭音频上不受监督的训练
Jialu Li1,2, Mark Hasegawa-Johnson1,2, Nancy L McElwain2,3
1Department of Electrical and Computer Engineering, University of Illinois.
概括
这项研究介绍了LittleBeats (LB),一个婴儿可穿戴设备,使用wav2vec 2.0 (W2V2) 进行家庭音频分析. 在家中录制的W2V2预训练改善了婴儿的扬声器日记和发音分类.
科学领域:
- 语音处理 语音处理
- 机器学习是机器学习.
- 婴儿行为分析 婴儿行为分析
背景情况:
- 自动家庭音频分析需要强大的语音处理模型.
- 以前的方法使用了通用嵌入式和监督学习.
- 婴儿可穿戴设备为数据收集提供了新的途径.
研究的目的:
- 为了增强LittleBeats (LB) 婴儿可穿戴设备的音频分析功能.
- 调查wave2vec 2.0 (W2V2) 预训练在家庭录音中家庭音频表现的有效性.
- 将W2V2性能与有限的标记数据与大型未标记数据集进行比较.
主要方法:
- 预训 wav2vec 2.0 (W2V2) 在1k小时的未标记的家庭录音上从LB设备.
- 微调W2V2用于扬声器日记化 (SD) 和发音分类 (VC) 任务.
- 使用SpecAugment和环境语音腐败来提高模型的稳定性.
主要成果:
- 在1k小时的家庭录音上预训练W2V2的表现优于在LB数据上预训练52k小时的一般音频的模型.
- 在扬声器日记化 (SD) 中获得了12%的相对收益,并且在发音分类 (VC) 中获得了适度的提升.
- 外部未标记和标记的数据进一步增强了W2V2的预训练和微调.
结论:
- 针对特定领域的家庭录音的W2V2预训对于婴儿音频分析是有效的.
- 与大型的一般数据集相比,有限的相关数据可以产生更好的结果.
- LittleBeats设备和W2V2模型为家庭音频分析提供了一个有希望的方法.
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