ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge
Not on the map. Why?
Abstract (arXiv)
Speech emotion recognition (SER) in naturalistic settings remains a challenge due to the intrinsic variability, diverse recording conditions, and class imbalance. As participants in the Interspeech Naturalistic SER Challenge which focused on these complexities, we present Abhinaya, a system integrating speech-based, text-based, and speech-text models. Our approach fine-tunes self-supervised and speech large language models (SLLM) for speech representations, leverages large language models (LLM) for textual context, and employs speech-text modeling with an SLLM to capture nuanced emotional cues. To combat class imbalance, we apply tailored loss functions and generate categorical decisions through majority voting. Despite one model not being fully trained, the Abhinaya system ranked 4th among 166 submissions. Upon completion of training, it achieved state-of-the-art performance among published results, demonstrating the effectiveness of our approach for SER in real-world conditions.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 23 May 2025.
Builds on
27 of 41 references.
References · 27
- 2023GPT-4 Technical Reportcited by 18,396
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
- 2023Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Modelscited by 608
- 2023SALMONN: Towards Generic Hearing Abilities for Large Language Modelscited by 506
15 more
- 2022BEATs: Audio Pre-Training with Acoustic Tokenizerscited by 388
- 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 268
- 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 162
- 2020CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordingscited by 132
- 2018Attentive Statistics Pooling for Deep Speaker Embeddingcited by 114
- 2024WavLLM: Towards Robust and Adaptive Speech Large Language Modelcited by 109
- 2021Label-Imbalanced and Group-Sensitive Classification under Overparameterizationcited by 74
- 2020The Third DIHARD Diarization Challengecited by 49
- 2021DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acousticscited by 21
- 2023HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognitioncited by 11
- 2019The VOiCES from a Distance Challenge 2019 Evaluation Plancited by 10
- 2025LLM supervised Pre-training for Multimodal Emotion Recognition in Conversationscited by 9
- 2021The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acousticscited by 8
- 2023Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environmentscited by 4
- 2023The 2022 NIST Language Recognition Evaluationcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.