arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.
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The paper introduces ACERT, a module that incorporates a flexible-length window of conversational context to enhance Speech Emotion Recognition (SER). By capturing emotional evolution across utterances, ACERT outperforms state‑of‑the‑art methods on IEMOCAP, sets a new context‑aware benchmark on SAFE, and achieves strong results on MELD. Ablation studies attribute ACERT’s improvements to emotional and conversational continuity rather than speaker identity or acoustic conditions.
arXiv:2606. 00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues.
The paper introduces DSSM-CRF, an audio‑only architecture for conversational speech emotion recognition that separates cross‑speaker contextual influence from within‑speaker emotion evolution. It uses bidirectional state‑space models to encode fused self‑supervised speech representations at both frame and dialogue scales, then orders each speaker’s utterances into an independent dynamic conditional random field chain. The model achieves state‑of‑the‑art performance on IEMOCAP and MELD, with complementary gains from speaker‑wise factorization and CRF modeling.
arXiv:2607. 03744v1 Announce Type: new Abstract: Automatic depression detection from clinical interviews typically models the semantic content and acoustic characteristics of participant speech.
arXiv:2606. 07293v1 Announce Type: cross Abstract: Speech Emotion Conversion (SEC) aims to transform the emotion of a source utterance into a target emotion while preserving content and speaker identity.
arXiv:2607. 15755v1 Announce Type: cross Abstract: Conversational Speech Synthesis (CSS) aims to synthesize speech with human-like emotional expression and contextual consistency in user-agent interactions.