1. Introduction
2. Overall
   • 1. The organization of this survey
   • 2. General classification of spoken dialogue systems
   • 3. Key capabilities of speech dialogue systems
   • 4. Publicly Available Speech Dialogue Models
3. Representations of Spoken Dialogue Models
4. Training Paradigm of Spoken Dialogue Model
5. Streaming, Duplex, and Interaction
6. Training Resources and Evaluation
   • 1. Training resources
   • 2. Evaluation
7. Cite

• 2024.11.22: We release WavChat (A survey of spoken dialogue models about 60 pages) on arxiv! 🎉
• 2024.08.31: We release WavTokenizer on arxiv.

This repository is the official repository of the WavChat: A Survey of Spoken Dialogue Models .

Figure 1: The timeline of existing spoken dialogue models in recent years.

Abstract

Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. In the broader context of multimodal models, the speech modality offers a direct interface for human-computer interaction, enabling direct communication between AI and users. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Moreover, they generate high-quality, multi-turn speech responses with low latency, enabling real-time interaction through simultaneous listening and speaking capability. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems and the underlying technologies. To address this, we have first compiled existing spoken dialogue systems in the chronological order and categorized them into the cascaded and end-to-end paradigms. We then provide an in-depth overview of the core technologies in spoken dialogue models, covering aspects such as speech representation, training paradigm, streaming, duplex, and interaction capabilities. Each section discusses the limitations of these technologies and outlines considerations for future research. Additionally, we present a thorough review of relevant datasets, evaluation metrics, and benchmarks from the perspectives of training and evaluating spoken dialogue systems. We hope this survey will contribute to advancing both academic research and industrial applications in the field of spoken dialogue systems.

Figure 2: Orgnization of this survey.

Figure 3: A general overview of current spoken dialogue systems.

Figure 4: An overview of the spoken dialogue systems' nine ideal capabilities.

In the section Representations of Spoken Dialogue Models, we provide insights into how to represent the data in a speech dialogue model for better understanding and generation of speech. The choice of representation method directly affects the model's effectiveness in processing speech signals, system performance, and range of applications. The section covers two main types of representations: semantic representations and acoustic representations.

In the Training Paradigm of Spoken Dialogue Model section, we focuse on how to adapt text-based large language models (LLMs) into dialogue systems with speech processing capabilities. The selection and design of training paradigms have a direct impact on the performance, real-time performance, and multimodal alignment of the model.

The Streaming, Duplex, and Interaction section mainly discusses the implementation of streaming processing, duplex communication, and interaction capabilities inspeech dialogue models. These features are crucial for improving the response speed, naturalness, and interactivity of the model in real-time conversations.

Figure 7: The Example Diagram of Duplex Interaction

Evaluation is a crucial aspect of training and testing spoken dialogue models. In this section, we provide a comprehensive overview of the evaluation from 11 aspects. The evaluation metrics are categorized into two main types: Basic Evaluation, and Advanced Evaluation.

@article{ji2024wavchat,
  title={WavChat: A Survey of Spoken Dialogue Models},
  author={Ji, Shengpeng and Chen, Yifu and Fang, Minghui and Zuo, Jialong and Lu, Jingyu and Wang, Hanting and Jiang, Ziyue and Zhou, Long and Liu, Shujie and Cheng, Xize and others},
  journal={arXiv preprint arXiv:2411.13577},
  year={2024}
}