Building a Taiwanese Mandarin Spoken Language Model: A First Attempt

• URL: http://arxiv.org/abs/2411.07111v1
• Date: Mon, 11 Nov 2024 16:37:40 GMT
• Title: Building a Taiwanese Mandarin Spoken Language Model: A First Attempt
• Authors: Chih-Kai Yang, Yu-Kuan Fu, Chen-An Li, Yi-Cheng Lin, Yu-Xiang Lin, Wei-Chih Chen, Ho Lam Chung, Chun-Yi Kuan, Wei-Ping Huang, Ke-Han Lu, Tzu-Quan Lin, Hsiu-Hsuan Wang, En-Pei Hu, Chan-Jan Hsu, Liang-Hsuan Tseng, I-Hsiang Chiu, Ulin Sanga, Xuanjun Chen, Po-chun Hsu, Shu-wen Yang, Hung-yi Lee,
• Abstract summary: This report aims to build a large spoken language model (MLL) for Taiwanese Mandarin tailored to enable realtime speech interaction in multi-turn conversations. Our end-end model incorporates a decoder-only transformer architecture and aims to achieve seamless interaction while preserving conversational fluency flow.
• Score: 44.54200115439157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech interaction in multi-turn conversations. Our end-to-end model incorporates a decoder-only transformer architecture and aims to achieve seamless interaction while preserving the conversational flow, including full-duplex capabilities allowing simultaneous speaking and listening. The paper also details the training process, including data preparation with synthesized dialogues and adjustments for real-time interaction. We also developed a platform to evaluate conversational fluency and response coherence in multi-turn dialogues. We hope the release of the report can contribute to the future development of spoken LLMs in Taiwanese Mandarin.

Related papers

• OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation [24.68804661538364]
  Full spoken dialogue systems significantly mirror human-human interactions. achieving low latency and natural interactions is a significant challenge. End-to-end full-to-end spoken dialogue systems are a promising direction for developing efficient and natural end-to-end systems. Audio samples of dialogues generated by OmniFlatten can be found at this web site.
  arXiv  Detail & Related papers  (2024-10-23T11:58:58Z)
• Let's Go Real Talk: Spoken Dialogue Model for Face-to-Face Conversation [55.043492250775294]
  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.
  arXiv  Detail & Related papers  (2024-06-12T04:48:36Z)
• Integrating Paralinguistics in Speech-Empowered Large Language Models for Natural Conversation [46.93969003104427]
  This paper introduces an extensive speech-text LLM framework, the Unified Spoken Dialog Model (USDM) USDM is designed to generate coherent spoken responses with naturally occurring prosodic features relevant to the given input speech. Our approach effectively generates natural-sounding spoken responses, surpassing previous and cascaded baselines.
  arXiv  Detail & Related papers  (2024-02-08T14:35:09Z)
• Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model [8.180382743037082]
  This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously.
  arXiv  Detail & Related papers  (2023-09-20T01:48:27Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech [58.93395189153713]
  We extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks. We propose a speech-text joint pretraining framework, where we randomly mask the spectrogram and the phonemes. Our model shows great improvements over speaker-embedding-based multi-speaker TTS methods.
  arXiv  Detail & Related papers  (2022-11-07T13:35:16Z)
• Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling [80.51094098799736]
  We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder. BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
  arXiv  Detail & Related papers  (2022-04-18T03:51:46Z)

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.