# BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport

arXiv: https://arxiv.org/abs/2609.27615 · HTML: https://mlsanity.com/paper/2609.27615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 16 of 16)

- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information](https://mlsanity.com/paper/2006.12013/) (2020)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [Enriching Multimodal Sentiment Analysis through Textual Emotional Descriptions of Visual-Audio Content](https://mlsanity.com/paper/2412.10460/) (2024)
- [Enhancing Multimodal Emotion Recognition through Multi-Granularity Cross-Modal Alignment](https://mlsanity.com/paper/2412.20821/) (2024)
- [Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier](https://mlsanity.com/paper/2510.23506/) (2025)
- [When Tone and Words Disagree: Towards Robust Speech Emotion Recognition under Acoustic-Semantic Conflict](https://mlsanity.com/paper/2601.04564/) (2026)
- [AmbER2: Dual Ambiguity-Aware Emotion Recognition Applied to Speech and Text](https://mlsanity.com/paper/2601.18010/) (2026)
- [Cross-modal Prompting for Balanced Incomplete Multi-modal Emotion Recognition](https://mlsanity.com/paper/2512.11239/) (2025)
- [Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning](https://mlsanity.com/paper/2511.07061/) (2025)
- [FINE: Factorized multimodal sentiment analysis via mutual INformation Estimation](https://mlsanity.com/paper/2511.20167/) (2025)

## Abstract (arXiv)

In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text MER, affective cues are often entangled with speaker style and lexical content, while cross-modal disagreement further complicates how the evidence should be integrated. Under conventional discriminative fusion, multimodal evidence is compressed into a terminal prediction, with modality-specific cues and conflict information insufficiently preserved. In large generative affective models, by contrast, affective reasoning is typically embedded in language decoding, leaving emotion evidence implicit and difficult to verify in a structured space. To address these limitations, BiCFlow-MER (Bidirectional Conditional Flow for Multimodal Emotion Recognition) is proposed as a conditional-flow framework in which audio-text MER is formulated as generative evidence transport within a structured emotion space. Within BiCFlow-MER, emotion-oriented evidence is disentangled from speaker-style and lexical-content factors to construct a conflict-aware affective condition. Guided by this condition, each utterance is transported to an explicit emotion-space endpoint through a bidirectional rectified flow. Candidate emotions are jointly verified through adaptive prototype-cloud scoring of the transported endpoint and backward class-to-condition consistency with the original multimodal condition, enabling conflict-aware recognition. BiCFlow-MER is shown to outperform all compared methods across IEMOCAP, MELD, and the zero-shot CASE benchmark. By orchestrating discriminative recognition and generative evidence modeling through conditional transport, BiCFlow-MER defines a new MER paradigm.