Contrastive Unsupervised Learning for Speech Emotion Recognition
Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled datasets. To circumvent this problem, we investigate how unsupervised representation learning on unlabeled datasets can benefit SER. We show that the contrastive predictive coding (CPC) method can learn salient representations from unlabeled datasets, which improves emotion recognition performance. In our experiments, this method achieved state-of-the-art concordance correlation coefficient (CCC) performance for all emotion primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on the MSP- Podcast dataset, our method obtained considerable performance improvements compared to baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionRepresentation LearningSpeech Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations
Considering the abundance of unlabeled speech data and the high labeling costs, unsupervised learning methods can be essential for better system development. One of the most successful methods is contrastive self-supervi…
Emotion RecognitionSelf-Supervised LearningSpeaker VerificationCochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition
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 i…
Contrastive LearningEmotion RecognitionImage AugmentationSelf-Supervised Learning+4Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition
Cross-corpus speech emotion recognition (SER) seeks to generalize the ability of inferring speech emotion from a well-labeled corpus to an unlabeled one, which is a rather challenging task due to the significant discrepa…
Cross-corpusDomain AdaptationEmotion RecognitionSpeech Emotion Recognition+1Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across Languages
Multilingual speech emotion recognition aims to estimate a speaker's emotional state using a contactless method across different languages. However, variability in voice characteristics and linguistic diversity poses sig…
Contrastive LearningDiversityEmotion RecognitionSpeech Emotion RecognitionLearning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised le…
Contrastive LearningEmotion RecognitionSpeech Emotion Recognition