1 citations · 2 across the 10 of their papers we have counts for
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MultiGen: Child-Friendly Multilingual Speech Generator with LLMs
Xiaoxue Gao, Huayun Zhang, Nancy F. Chen
Generative speech models have demonstrated significant potential in improving human-machine interactions, offering valuable real-world applications such as language learning for ch…
Prompt-Unseen-Emotion: Zero-shot Expressive Speech Synthesis with Prompt-LLM Contextual Knowledge for Mixed Emotions
Xiaoxue Gao, Huayun Zhang, Nancy F. Chen
Existing expressive text-to-speech (TTS) systems primarily model a limited set of categorical emotions, whereas human conversations extend far beyond these predefined emotions, mak…
Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization
Xiaoxue Gao, Chen Zhang, Yiming Chen +2
Current emotional text-to-speech (TTS) models predominantly conduct supervised training to learn the conversion from text and desired emotion to its emotional speech, focusing on a…
PRESENT: Zero-Shot Text-to-Prosody Control
Perry Lam, Huayun Zhang, Nancy F. Chen +2
Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable ze…
SNIPER Training: Single-Shot Sparse Training for Text-to-Speech
Perry Lam, Huayun Zhang, Nancy F. Chen +2
Text-to-speech (TTS) models have achieved remarkable naturalness in recent years, yet like most deep neural models, they have more parameters than necessary. Sparse TTS models can…
EPIC TTS Models: Empirical Pruning Investigations Characterizing Text-To-Speech Models
Perry Lam, Huayun Zhang, Nancy F. Chen +1
Neural models are known to be over-parameterized, and recent work has shown that sparse text-to-speech (TTS) models can outperform dense models. Although a plethora of sparse metho…