Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal and frequency masking, have emerged, yielding superior performance compared to classic approaches drawn from the image augmentation domain. Our proposed contribution builds upon this successful paradigm by introducing CochCeps-Augment, a novel bio-inspired masking augmentation task for self-supervised contrastive learning of speech representations. Specifically, we utilize the newly introduced bio-inspired cochlear cepstrogram (CCGRAM) to derive noise robust representations of input speech, that are then further refined through a self-supervised learning scheme. The latter employs SimCLR to generate contrastive views of a CCGRAM through masking of its angle and quefrency dimensions. Our experimental approach and validations on the emotion recognition K-EmoCon benchmark dataset, for the first time via a speaker-independent approach, features unsupervised pre-training, linear probing and fine-tuning. Our results potentiate CochCeps-Augment to serve as a standard tool in speech emotion recognition analysis, showing the added value of incorporating bio-inspired masking as an informative augmentation task for self-supervision. Our code for implementing CochCeps-Augment will be made available at: https://github.com/GiannisZgs/CochCepsAugment.

翻译：自监督学习（SSL）在语音情感内容自动识别中易受噪声严重干扰，从而影响对语音复杂时频信息结构的建模效率。近年来，基于大规模语音数据集的SSL方法以及新型音频专用SSL代理任务（如时域和频域掩码）已展现出优于传统图像增强领域方法的性能。本文基于该成功范式提出CochCeps-Augment——一种新型生物启发式掩码增强任务，用于语音表征的自监督对比学习。具体而言，我们利用最新引入的生物启发式耳蜗倒谱图（CCGRAM）获取输入语音的噪声鲁棒表征，并通过自监督学习方案进一步优化。该方案采用SimCLR，通过对CCGRAM的角度和倒频率维度进行掩码来生成对比视图。我们在情感识别基准数据集K-EmoCon上首次采用说话人无关方法进行实验验证，包括无监督预训练、线性探测和微调。实验结果表明，CochCeps-Augment有望成为语音情感识别分析的标准工具，证实了将生物启发式掩码作为信息增强任务纳入自监督学习的附加价值。本文CochCeps-Augment的实现代码将发布于：https://github.com/GiannisZgs/CochCepsAugment。