Authors: Heeseung Kim, Soonshin Seo, Kyeongseok Jeong, Ohsung Kwon, Jungwhan Kim, Jaehong Lee, Eunwoo Song, Myungwoo Oh, Sungroh Yoon, Kang Min Yoo

Published on: February 08, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.05706

Summary

• What is new: A novel Unified Spoken Dialog Model (USDM) that generates coherent spoken responses without needing ASR or TTS technologies.
• Why this is important: The challenge in making language models interpret and synthesize speech directly for dialog purposes.
• What the research proposes: Introducing an LLM framework that combines multi-step speech-text inference with a generalized speech-text pretraining scheme for better cross-modal semantics understanding.
• Results: The approach significantly outperforms existing models in producing natural-sounding spoken responses, showcasing improved robustness and speech quality.

Technical Details

Technological frameworks used: Unified Spoken Dialog Model (USDM)

Models used: Chain-of-reasoning LLM

Data used: Cross-modal speech and text data

Potential Impact

Voice assistant technology providers, customer service automation sectors, and companies involved in speech recognition and synthesis technologies.

Want to implement this idea in a business?

We have generated a startup concept here: VoiceFlow.