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EmoSphere-TTS: Emotional Style and Intensity Modeling via Spherical Emotion Vector for Controllable Emotional Text-to-Speech
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Despite rapid advances in the field of emotional text-to-speech (TTS), recent studies primarily focus on mimicking the average style of a particular emotion. As a result, the ability to manipulate speech emotion remains constrained to several predefined labels, compromising the ability to reflect the nuanced variations of emotion. In this paper, we propose EmoSphere-TTS, which synthesizes expressive emotional speech by using a spherical emotion vector to control the emotional style and intensity of the synthetic speech. Without any human annotation, we use the arousal, valence, and dominance pseudo-labels to model the complex nature of emotion via a Cartesian-spherical transformation. Furthermore, we propose a dual conditional adversarial network to improve the quality of generated speech by reflecting the multi-aspect characteristics. The experimental results demonstrate the model ability to control emotional style and intensity with high-quality expressive speech.
Forward citations
Cited by 6 Pith papers
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Emo-LiPO: Listwise Preference Optimization for Fine-Grained Emotion Intensity Control in LLM-based Text-to-Speech
Emo-LiPO applies listwise preference optimization to model global emotion intensity ordering in LLM TTS, yielding better accuracy and controllability than supervised or DPO baselines on a new multi-speaker dataset.
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CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering
Activation steering at mid-to-late language-module layers of hybrid TTS models enables composable mixed-emotion and text-mismatched emotional speech without retraining.
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Beyond One-Size-Fits-All: Personalized and Culturally Adaptive Emotional TTS via Interactive Optimization of Individual Emotion Perception Spaces
An interactive genetic algorithm tunes arousal-valence coordinates per listener in emotional TTS, and personalized or culture-specific coordinates beat a generic U.S.-average baseline in small A/B tests.
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Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
An adaptive CFG method that tunes guidance based on LLM-detected mismatch between emotion prompts and text semantics improves emotional expressiveness in AR TTS while preserving audio quality and intelligibility.
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Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
Introduces CCG-CFG with inconsistency-based dynamic scales and hard-sample mining distillation to boost emotional alignment in auto-regressive TTS, reporting up to 12% absolute gains in emotion recognition accuracy.
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Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
An emotion TTS system adjusts Classifier-Free Guidance strength according to text-style semantic mismatch; it shows small emotion-accuracy gains, but headline baselines and subjective results are absent from the main text.
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