An official website of the United States government
Speech emotion recognition (SER) is a challenging task due to the limited availability of real-world labeled datasets. Since it is easier to find unlabeled data, the use of self-supervised learning (SSL) has become an attractive alternative. This study proposes new pre-text tasks for SSL to improve SER. While our target application is SER, the proposed pre-text tasks include audio-visual formulations, leveraging the relationship between acoustic and facial features. Our proposed approach introduces three new unimodal and multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech. Task 1 predicts energy variations (high or low) from a speech sequence. Task 2 uses speech features to predict facial activation (high or low) based on facial landmark movements. Task 3 performs a multi-class emotion recognition task on emotional labels obtained from combinations of action units (AUs) detected across a video sequence. We pre-train a network with 60.92 hours of unlabeled data, fine-tuning the model for the downstream SER task. The results on the CREMA-D dataset show that the model pre-trained on the proposed domain-specific pre-text tasks significantly improves the precision (up to 5.1%), recall (up to 4.5%), and F1-scores (up to 4.9%) of our SER system.
more »
« less
This content will become publicly available on April 6, 2026
Noise-Robust Speech Emotion Recognition Using Shared Self-Supervised Representations with Integrated Speech Enhancement
Recent studies have demonstrated the effectiveness of fine-tuning self-supervised speech representation models for speech emotion recognition (SER). However, applying SER in real-world environments remains challenging due to pervasive noise. Relying on low-accuracy predictions due to noisy speech can undermine the user’s trust. This paper proposes a unified self-supervised speech representation framework for enhanced speech emotion recognition designed to increase noise robustness in SER while generating enhanced speech. Our framework integrates speech enhancement (SE) and SER tasks, leveraging shared self-supervised learning (SSL)-derived features to improve emotion classification performance in noisy environments. This strategy encourages the SE module to enhance discriminative information for SER tasks. Additionally, we introduce a cascade unfrozen training strategy, where the SSL model is gradually unfrozen and fine-tuned alongside the SE and SER heads, ensuring training stability and preserving the generalizability of SSL representations. This approach demonstrates improvements in SER performance under unseen noisy conditions without compromising SE quality. When tested at a 0 dB signal-to-noise ratio (SNR) level, our proposed method outperforms the original baseline by 3.7% in F1-Macro and 2.7% in F1-Micro scores, where the differences are statistically significant.
more »
« less
- Award ID(s):
- 2016719
- PAR ID:
- 10655472
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Hyderabad, India
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
na (Ed.)Deep clustering is a popular unsupervised technique for feature representation learning. We recently proposed the chunk-based DeepEmoCluster framework for speech emotion recognition (SER) to adopt the concept of deep clustering as a novel semi-supervised learning (SSL) framework, which achieved improved recognition performances over conventional reconstruction-based approaches. However, the vanilla DeepEmoCluster lacks critical sentence- level temporal information that is useful for SER tasks. This study builds upon the DeepEmoCluster framework, creating a powerful SSL approach that leverages temporal information within a sentence. We propose two sentence-level temporal modeling alternatives using either the temporal-net or the triplet loss function, resulting in a novel temporal-enhanced DeepEmoCluster framework to capture essential temporal information. The key contribution to achieving this goal is the proposed sentence-level uniform sampling strategy, which preserves the original temporal order of the data for the clustering process. An extra network module (e.g., gated recurrent unit) is utilized for the temporal-net option to encode temporal information across the data chunks. Alternatively, we can impose additional temporal constraints by using the triplet loss function while training the DeepEmoCluster framework, which does not increase model complexity. Our experimental results based on the MSP-Podcast corpus demonstrate that the proposed temporal-enhanced framework significantly outperforms the vanilla DeepEmoCluster framework and other existing SSL approaches in regression tasks for the emotional attributes arousal, dominance, and valence. The improvements are observed in fully-supervised learning or SSL implementations. Further analyses validate the effectiveness of the proposed temporal modeling, showing (1) high temporal consistency in the cluster assignment, and (2) well-separated emotional patterns in the generated clusters.more » « less
-
null (Ed.)Semi-supervised learning (SSL) is an appealing approach to resolve generalization problem for speech emotion recognition (SER) systems. By utilizing large amounts of unlabeled data, SSL is able to gain extra information about the prior distribution of the data. Typically, it can lead to better and robust recognition performance. Existing SSL approaches for SER include variations of encoder-decoder model structures such as autoencoder (AE) and variational autoencoders (VAEs), where it is difficult to interpret the learning mechanism behind the latent space. In this study, we introduce a new SSL framework, which we refer to as the DeepEmoCluster framework, for attribute-based SER tasks. The DeepEmoCluster framework is an end-to-end model with mel-spectrogram inputs, which combines a self-supervised pseudo labeling classification network with a supervised emotional attribute regressor. The approach encourages the model to learn latent representations by maximizing the emotional separation of K-means clusters. Our experimental results based on the MSP-Podcast corpus indicate that the DeepEmoCluster framework achieves competitive prediction performances in fully supervised scheme, outperforming baseline methods in most of the conditions. The approach can be further improved by incorporating extra unlabeled set. Moreover, our experimental results explicitly show that the latent clusters have emotional dependencies, enriching the geometric interpretation of the clusters.more » « less
-
It is difficult to achieve robust and well-generalized models for tasks involving subjective concepts such as emotion. It is inevitable to deal with noisy labels, given the ambiguous nature of human perception. Methodologies relying on semi-supervised learning (SSL) and curriculum learning have been proposed to enhance the generalization of the models. This study proposes a novel deep mutual information (DeepMI) metric, built with the SSL pre-trained DeepEmoCluster framework to establish the difficulty of samples. The DeepMI metric quantifies the relationship between the acoustic patterns and emotional attributes (e.g., arousal, valence, and dominance). The DeepMI metric provides a better curriculum, achieving state-of-the-art performance that is higher than results obtained with existing curriculum metrics for speech emotion recognition (SER). We evaluate the proposed method with three emotional datasets in matched and mismatched testing conditions. The experimental evaluations systematically show that a model trained with the DeepMI metric not only obtains competitive generalization performances, but also maintains convergence stability. Furthermore, the extracted DeepMI values are highly interpretable, reflecting information ranks of the training samples.more » « less
-
Self-supervised learning (SSL) for rich speech representations has achieved empirical success in low-resource Automatic Speech Recognition (ASR) and other speech processing tasks, which can mitigate the necessity of a large amount of transcribed speech and thus has driven a growing demand for on-device ASR and other speech processing. However, advanced speech SSL models have become increasingly large, which contradicts the limited on-device resources. This gap could be more severe in multilingual/multitask scenarios requiring simultaneously recognizing multiple languages or executing multiple speech processing tasks. Additionally, strongly overparameterized speech SSL models tend to suffer from overfitting when being finetuned on low-resource speech corpus. This work aims to enhance the practical usage of speech SSL models towards a win-win in both enhanced efficiency and alleviated overfitting via our proposed S-Router framework, which for the first time discovers that simply discarding no more than 10% of model weights via only finetuning model connections of speech SSL models can achieve better accuracy over standard weight finetuning on downstream speech processing tasks. More importantly, S-Router can serve as an all-in-one technique to enable (1) a new finetuning scheme, (2) an efficient multilingual/multitask solution, (3) a state-of-the-art pruning technique, and (4) a new tool to quantitatively analyze the learned speech representation.more » « less
