Summary of Style-talker: Finetuning Audio Language Model and Style-based Text-to-speech Model For Fast Spoken Dialogue Generation, by Yinghao Aaron Li et al.
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation
by Yinghao Aaron Li, Xilin Jiang, Jordan Darefsky, Ge Zhu, Nima Mesgarani
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Style-Talker, a framework for end-to-end speech-to-speech conversation bots. Large language models (LLMs) have enabled text-based chatbots to engage in coherent dialogues, but extending this to spoken conversations remains challenging due to dataset and computational requirements. The conventional approach using automatic speech recognition (ASR), LLM, and text-to-speech (TTS) models suffers from unnatural prosody and latency. Style-Talker fine-tunes an audio LLM alongside a style-based TTS model for fast spoken dialog generation. It takes user input audio, transcribed chat history, and speech styles to generate the speaking style and text response. The framework accelerates traditional cascade ASR-LLM-TTS systems while integrating paralinguistic information from input speech. Experimental results show that Style-Talker outperforms baselines in terms of dialogue naturalness and coherence while being faster. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new chatbot that can talk to people using only voices, without needing text first. Right now, we have chatbots that can understand and respond to text messages, but talking to each other is harder because it needs more data and computing power. The usual way of doing this involves recognizing what someone says, then translating it into text, and finally speaking the response back out. This process takes time and doesn’t sound natural. The new system, called Style-Talker, does things differently by generating spoken responses based on what was said earlier. It’s faster and sounds more like real conversations. |
