Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation

• URL: http://arxiv.org/abs/2406.07867v2
• Date: Fri, 2 Aug 2024 15:05:47 GMT
• Title: Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation
• Authors: Se Jin Park, Chae Won Kim, Hyeongseop Rha, Minsu Kim, Joanna Hong, Jeong Hun Yeo, Yong Man Ro,
• Abstract summary: We introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response. We also introduce MultiDialog, the first large-scale multimodal spoken dialogue corpus.
• Score: 55.043492250775294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce a novel Face-to-Face spoken dialogue model. It processes audio-visual speech from user input and generates audio-visual speech as the response, marking the initial step towards creating an avatar chatbot system without relying on intermediate text. To this end, we newly introduce MultiDialog, the first large-scale multimodal (i.e., audio and visual) spoken dialogue corpus containing 340 hours of approximately 9,000 dialogues, recorded based on the open domain dialogue dataset, TopicalChat. The MultiDialog contains parallel audio-visual recordings of conversation partners acting according to the given script with emotion annotations, which we expect to open up research opportunities in multimodal synthesis. Our Face-to-Face spoken dialogue model incorporates a textually pretrained large language model and adapts it into the audio-visual spoken dialogue domain by incorporating speech-text joint pretraining. Through extensive experiments, we validate the effectiveness of our model in facilitating a face-to-face conversation. Demo and data are available at https://multidialog.github.io and https://huggingface.co/datasets/IVLLab/MultiDialog, respectively.

Related papers

• Moshi: a speech-text foundation model for real-time dialogue [78.88479749811376]
  Current systems for spoken dialogue rely on pipelines independent voice activity detection and text-to-speech. We show how Moshi Moshi can provide streaming speech recognition and text-to-speech. Our resulting model is first real-time full spoken large language model modality.
  arXiv  Detail & Related papers  (2024-09-17T17:55:39Z)
• Audio Dialogues: Dialogues dataset for audio and music understanding [29.550656226658962]
  We introduce Audio Dialogues: a multi-turn dialogue dataset containing 163.8k samples for general audio sounds and music. In addition to dialogues, Audio Dialogues also has question-answer pairs to understand and compare multiple input audios together.
  arXiv  Detail & Related papers  (2024-04-11T10:08:34Z)
• Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
  This paper proposes to model multi-turn dialogues from a topic-aware perspective. We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
  arXiv  Detail & Related papers  (2023-09-18T11:03:55Z)
• ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human [76.62897301298699]
  ChatPLUG is a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format. We show that modelname outperforms state-of-the-art Chinese dialogue systems on both automatic and human evaluation. We deploy modelname to real-world applications such as Smart Speaker and Instant Message applications with fast inference.
  arXiv  Detail & Related papers  (2023-04-16T18:16:35Z)
• TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World [97.58623810402563]
  We introduce a new video-based multi-modal dialogue dataset, called TikTalk. We collect 38K videos from a popular video-sharing platform, along with 367K conversations posted by users beneath them. Users engage in spontaneous conversations based on their multi-modal experiences from watching videos, which helps recreate real-world chitchat context.
  arXiv  Detail & Related papers  (2023-01-14T10:18:22Z)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization [19.918194137007653]
  We present a pre-training framework for long dialogue understanding and summarization. Considering the nature of long conversations, we propose a window-based denoising approach for generative pre-training. We conduct extensive experiments on five datasets of long dialogues, covering tasks of dialogue summarization, abstractive question answering and topic segmentation.
  arXiv  Detail & Related papers  (2021-09-06T13:55:03Z)
• MMChat: Multi-Modal Chat Dataset on Social Media [8.904627457711683]
  MMChat is a large scale multi-modal dialogue corpus (32.4M raw dialogues and 120.84K filtered dialogues) Unlike previous corpora that are crowd-sourced or collected from fictitious movies, MMChat contains image-grounded dialogues collected from real conversations on social media. We develop a benchmark model to address this issue in dialogue generation tasks by adapting the attention routing mechanism on image features.
  arXiv  Detail & Related papers  (2021-08-16T15:27:49Z)
• OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts [35.57757367869986]
  We release bf OpenViDial, a large-scale multi- module dialogue dataset. OpenViDial contains a total number of 1.1 million dialogue turns. We propose a family of encoder-decoder models leveraging both textual and visual contexts.
  arXiv  Detail & Related papers  (2020-12-30T03:02:50Z)
• Interview: A Large-Scale Open-Source Corpus of Media Dialog [11.28504775964698]
  We introduce 'Interview': a large-scale (105K conversations) media dialog dataset collected from news interview transcripts. Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance. 'Interview' contains speaker role annotations for each turn, facilitating the development of engaging, responsive dialog systems.
  arXiv  Detail & Related papers  (2020-04-07T02:44:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.