An official website of the United States government
Speech Emotion Recognition (SER) faces a distinct challenge compared to other speech-related tasks because the annotations will show the subjective emotional perceptions of different annotators. Previous SER studies often view the subjectivity of emotion perception as noise by using the majority rule or plurality rule to obtain the consensus labels. However, these standard approaches overlook the valuable information of labels that do not agree with the consensus and make it easier for the test set. Emotion perception can have co-occurring emotions in realistic conditions, and it is unnecessary to regard the disagreement between raters as noise. To bridge the SER into a multi-label task, we introduced an “all-inclusive rule,” which considers all available data, ratings, and distributional labels as multi-label targets and a complete test set. We demonstrated that models trained with multi-label targets generated by the proposed AR outperform conventional single-label methods across incomplete and complete test sets.
more »
« less
Exploiting Co-occurrence Frequency of Emotions in Perceptual Evaluations To Train A Speech Emotion Classifier
Previous studies on speech emotion recognition (SER) with categorical emotions have often formulated the task as a single-label classification problem, where the emotions are considered orthogonal to each other. However, previous studies have indicated that emotions can co-occur, especially for more ambiguous emotional sentences (e.g., a mixture of happiness and sur- prise). Some studies have regarded SER problems as a multi-label task, predicting multiple emotional classes. However, this formulation does not leverage the relation between emotions during training, since emotions are assumed to be independent. This study explores the idea that emotional classes are not necessarily independent and its implications on training SER models. In particular, we calculate the frequency of co-occurring emotions from perceptual evaluations in the train set to generate a matrix with class-dependent penalties, punishing more mistakes between distant emotional classes. We integrate the penalization matrix into three existing label-learning approaches (hard-label, multi-label, and distribution-label learn- ing) using the proposed modified loss. We train SER models using the penalty loss and commonly used cost functions for SER tasks. The evaluation of our proposed penalization matrix on the MSP-Podcast corpus shows important relative improvements in macro F1-score for hard-label learning (17.12%), multi-label learning (12.79%), and distribution-label learning (25.8%).
more »
« less
- Award ID(s):
- 2016719
- PAR ID:
- 10441265
- Date Published:
- Journal Name:
- Interspeech 2022
- Page Range / eLocation ID:
- 161 to 165
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
na (Ed.)The problem of predicting emotional attributes from speech has often focused on predicting a single value from a sentence or short speaking turn. These methods often ignore that natural emotions are both dynamic and dependent on context. To model the dynamic nature of emotions, we can treat the prediction of emotion from speech as a time-series problem. We refer to the problem of predicting these emotional traces as dynamic speech emotion recognition. Previous studies in this area have used models that treat all emotional traces as coming from the same underlying distribution. Since emotions are dependent on contextual information, these methods might obscure the context of an emotional interaction. This paper uses a neural process model with a segment-level speech emotion recognition (SER) model for this problem. This type of model leverages information from the time-series and predictions from the SER model to learn a prior that defines a distribution over emotional traces. Our proposed model performs 21% better than a bidirectional long short-term memory (BiLSTM) baseline when predicting emotional traces for valence.more » « less
-
The advancement of Speech Emotion Recognition (SER) is significantly dependent on the quality of emotional speech corpora used for model training. Researchers in the field of SER have developed various corpora by adjusting design parameters to enhance the reliability of the training source. For this study, we focus on exploring communication modes of collection, specifically analyzing spontaneous emotional speech patterns gathered during conversation or monologue. While conversations are acknowledged as effective for eliciting authentic emotional expressions, systematic analyses are necessary to confirm their reliability as a better source of emotional speech data. We investigate this research question from perceptual differences and acoustic variability present in both emotional speeches. Our analyses on multi-lingual corpora show that, first, raters exhibit higher consistency for conversation recordings when evaluating categorical emotions, and second, perceptions and acoustic patterns observed in conversational samples align more closely with expected trends discussed in relevant emotion literature. We further examine the impact of these differences on SER modeling, which shows that we can train a more robust and stable SER model by using conversation data. This work provides comprehensive evidence suggesting that conversation may offer a better source compared to monologue for developing an SER model.more » « less
-
na (Ed.)In the field of affective computing, emotional annotations are highly important for both the recognition and synthesis of human emotions. Researchers must ensure that these emotional labels are adequate for modeling general human perception. An unavoidable part of obtaining such labels is that human annotators are exposed to known and unknown stimuli before and during the annotation process that can affect their perception. Emotional stimuli cause an affective priming effect, which is a pre-conscious phenomenon in which previous emotional stimuli affect the emotional perception of a current target stimulus. In this paper, we use sequences of emotional annotations during a perceptual evaluation to study the effect of affective priming on emotional ratings of speech. We observe that previous emotional sentences with extreme emotional content push annotations of current samples to the same extreme. We create a sentence-level bias metric to study the effect of affective priming on speech emotion recognition (SER) modeling. The metric is used to identify subsets in the database with more affective priming bias intentionally creating biased datasets. We train and test SER models using the full and biased datasets. Our results show that although the biased datasets have low inter-evaluator agreements, SER models for arousal and dominance trained with those datasets perform the best. For valence, the models trained with the less-biased datasets perform the best.more » « less
-
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
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.21437/Interspeech.2022-11041
