VQV Signal
Conversational text-to-speech (TTS) models improve naturalness and expressiveness but struggle with pronouncing rare, critical words like drug names. Phoneme adaptation techniques can provide better control over these pronunciations in cascaded ASR-LLM-TTS pipelines.
Conversational text-to-speech (TTS) models improve naturalness and expressiveness but struggle with pronouncing rare, critical words like drug names. Phoneme adaptation techniques can provide better control over these pronunciations in cascaded ASR-LLM-TTS pipelines.
AI-assisted summary based on the listed source.
Voice agents built as cascaded automatic speech recognition (ASR)->large language model (LLM)-> text-to-speech (TTS) pipelines increasingly rely on conversational TTS: streaming, context conditioned models that adapt delivery to dialogue history. Such models are natural and expressive but offer little explicit...
Accurate pronunciation of rare and trust-critical terms is essential for voice agents used in sensitive domains such as healthcare. Enhancing TTS models with phoneme adaptation improves reliability and user trust in voice-based AI systems.
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VQV surfaced this signal because it is recent, relevant to AI Voice, connected to arXiv.
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