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The Interspeech 2025 speech emotion recognition in natural istic conditions challenge builds on previous efforts to advance speech emotion recognition (SER) in real-world scenarios. The focus is on recognizing emotions from spontaneous speech, moving beyond controlled datasets. It provides a framework for speaker-independent training, development, and evaluation, with annotations for both categorical and dimensional tasks. The challenge attracted 93 research teams, whose models significantly improved state-of-the-art results over competitive baselines. This paper summarizes the challenge, focusing on the key outcomes. We analyze top-performing methods, emerging trends, and innovative directions. We highlight the effectiveness of combining foundational models based on audio and text to achieve robust SER systems. The competition website, with leaderboards, baseline code, and instructions, is available at: https://lab-msp.com/MSP-Podcast_Competition/IS2025/. 

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. 

An important task in human-computer interaction is to rank speech samples according to their expressive content. A preference learning framework is appropriate for obtaining an emotional rank for a set of speech samples. However, obtaining reliable labels for training a preference learning framework is a challenging task. Most existing databases provide sentence-level absolute attribute scores annotated by multiple raters, which have to be transformed to obtain preference labels. Previous studies have shown that evaluators anchor their absolute assessments on previously annotated samples. Hence, this study proposes a novel formulation for obtaining preference learning labels by only considering annotation trends assigned by a rater to consecutive samples within an evaluation session. The experiments show that the use of the proposed anchor-based ordinal labels leads to significantly better performance than models trained using existing alternative labels. 

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. 

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. 

Audio-visual emotion recognition (AVER) has been an important research area in human-computer interaction (HCI). Traditionally, audio-visual emotional datasets and corresponding models derive their ground truths from annotations obtained by raters after watching the audio-visual stimuli. This conventional method, however, neglects the nuanced human perception of emotional states, which varies when annotations are made under different emotional stimuli conditions—whether through unimodal or multimodal stimuli. This study investigates the potential for enhanced AVER system performance by integrating diverse levels of annotation stimuli, reflective of varying perceptual evaluations. We propose a two-stage training method to train models with the labels elicited by audio-only, face-only, and audio-visual stimuli. Our approach utilizes different levels of annotation stimuli according to which modality is present within different layers of the model, effectively modeling annotation at the unimodal and multi-modal levels to capture the full scope of emotion perception across unimodal and multimodal contexts. We conduct the experiments and evaluate the models on the CREMA-D emotion database. The proposed methods achieved the best performances in macro-/weighted-F1 scores. Additionally, we measure the model calibration, performance bias, and fairness metrics considering the age, gender, and race of the AVER systems. 

The increased ubiquitousness of small smart devices, such as cell- phones, tablets, smart watches and laptops, has led to unique user data, which can be locally processed. The sensors (e.g., microphones and webcam) and improved hardware of the new devices have al- lowed running deep learning models that 20 years ago would have been exclusive to high-end expensive machines. In spite of this progress, state-of-the-art algorithms for facial expression recognition (FER) rely on architectures that cannot be implemented on these devices due to computational and memory constraints. Alternatives involving cloud-based solutions impose privacy barriers that prevent their adoption or user acceptance in wide range of applications. This paper proposes a lightweight model that can run in real-time for image facial expression recognition (IFER) and video facial expression recognition (VFER). The approach relies on a personalization mechanism locally implemented for each subject by fine-tuning a central VFER model with unlabeled videos from a target subject. We train the IFER model to generate pseudo labels and we select the videos with the highest confident predictions to be used for adaptation. The adaptation is performed by implementing a federated learning strategy where the weights of the local model are averaged and used by the central VFER model. We demonstrate that this approach can improve not only the performance on the edge device providing personalized models to the users, but also the central VFER model. We implement a federated learning strategy where the weights of the local models are averaged and used by the central VFER. Within corpus and cross-corpus evaluations on two emotional databases demonstrate that edge models adapted with our personalization strategy achieve up to 13.1% gains in F1-scores. Furthermore, the federated learning implementation improves the mean micro F1-score of the central VFER model by up to 3.4%. The proposed lightweight solution is ideal for interactive user interfaces that preserve the data of the users. 

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in accessing or collecting naturalistic, large-scale, reliable emotional recordings. Also, the best practices for data collection are not necessarily described or shared when presenting emotional corpora. To address this issue, the paper proposes the creation of an affective naturalistic database consortium (AndC) that can encourage multidisciplinary cooperation among researchers and practitioners in the field of affective computing. This paper’s contribution is twofold. First, it proposes the design of the AndC with a customizable-standard framework for intelligently-controlled emotional data collection. The focus is on leveraging naturalistic spontaneous record- ings available on audio-sharing websites. Second, it presents as a case study the development of a naturalistic large-scale Taiwanese Mandarin podcast corpus using the customizable- standard intelligently-controlled framework. The AndC will en- able research groups to effectively collect data using the provided pipeline and to contribute with alternative algorithms or data collection protocols. 

Emotional annotation of data is important in affective computing for the analysis, recognition, and synthesis of emotions. As raters perceive emotion, they make relative comparisons with what they previously experienced, creating “anchors” that influence the annotations. This unconscious influence of the emotional content of previous stimuli in the perception of emotions is referred to as the affective priming effect. This phenomenon is also expected in annotations conducted with out-of-order segments, a common approach for annotating emotional databases. Can the affective priming effect introduce bias in the labels? If yes, how does this bias affect emotion recognition systems trained with these labels? This study presents a detailed analysis of the affective priming effect and its influence on speech emotion recognition (SER). The analysis shows that the affective priming effect affects emotional attributes and categorical emotion annotations. We observe that if annotators assign an extreme score to previous sentences for an emotional attribute (valence, arousal, or dominance), they will tend to annotate the next sentence closer to that extreme. We conduct SER experiments using the most biased sentences. We observe that models trained on the biased sentences perform the best and have the lowest prediction uncertainty. 

When selecting test data for subjective tasks, most studies define ground truth labels using aggregation methods such as the majority or plurality rules. These methods discard data points without consensus, making the test set easier than practical tasks where a prediction is needed for each sample. However, the discarded data points often express ambiguous cues that elicit coexisting traits perceived by annotators. This paper addresses the importance of considering all the annotations and samples in the data, highlighting that only showing the model’s performance on an incomplete test set selected by using the majority or plurality rules can lead to bias in the models’ performances. We focus on speech-emotion recognition (SER) tasks. We observe that traditional aggregation rules have a data loss ratio ranging from 5.63% to 89.17%. From this observation, we propose a flexible method named the all-inclusive aggregation rule to evaluate SER systems on the complete test data. We contrast traditional single-label formulations with a multi-label formulation to consider the coexistence of emotions. We show that training an SER model with the data selected by the all-inclusive aggregation rule shows consistently higher macro-F1 scores when tested in the entire test set, including ambiguous samples without agreement.