A Unified Speech LLM for Diarization and Speech Recognition in Multilingual Conversations

• URL: http://arxiv.org/abs/2507.02927v1
• Date: Thu, 26 Jun 2025 01:54:02 GMT
• Title: A Unified Speech LLM for Diarization and Speech Recognition in Multilingual Conversations
• Authors: Phurich Saengthong, Boonnithi Jiaramaneepinit, Sheng Li, Manabu Okumura, Takahiro Shinozaki,
• Abstract summary: We propose a unified speech LLM that jointly performs diarization and ASR in an end-to-end manner.<n>By reformulating the training data format and modifying the inference procedure, our model addresses the ambiguity inherent in pre-segmented audio.
• Score: 25.58593495281218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech Large Language Models (Speech LLMs) have emerged as a crucial paradigm in recent years, extending the capabilities of traditional LLMs to speech tasks such as automatic speech recognition (ASR) and spoken dialogue modeling. However, their effectiveness in real-world multilingual conversations remains limited by the scarcity of data that captures natural conversational phenomena. To address this, the MLC-SLM Challenge provides a multilingual conversational dataset and evaluates models on two tasks: ASR with oracle segmentation (Task I) and joint diarization and recognition without oracle information (Task II). In this paper, we focus on Task II and propose a unified speech LLM that jointly performs diarization and ASR in an end-to-end manner. By reformulating the training data format and modifying the inference procedure, our model addresses the ambiguity inherent in pre-segmented audio and achieves a 54.87\% relative improvement in tcpWER/tcpCER over the baseline, ranking 8th overall, despite using a smaller LLM backbone. We also report results from Task I using a fine-tuned speech LLM.

Related papers

• Enhancing Generalization of Speech Large Language Models with Multi-Task Behavior Imitation and Speech-Text Interleaving [36.246791887458194]
  Large language models (LLMs) have shown remarkable generalization across tasks.<n>LLMs typically use supervised fine-tuning to align speech with text-based LLMs.<n>We propose a novel multi-task 'behavior imitation' method with speech-text interleaving.
  arXiv  Detail & Related papers  (2025-05-24T11:09:13Z)
• Self-Powered LLM Modality Expansion for Large Speech-Text Models [62.27700381806554]
  Large language models (LLMs) exhibit remarkable performance across diverse tasks. This study aims to refine the use of speech datasets for LSM training by addressing the limitations of vanilla instruction tuning. We introduce a self-powered LSM that leverages augmented automatic speech recognition data generated by the model itself for more effective instruction tuning.
  arXiv  Detail & Related papers  (2024-10-04T04:34:24Z)
• DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data [84.01401439030265]
  Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs)<n>We present a simple yet effective automatic process for creating speech-text pair data.<n>Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data.
  arXiv  Detail & Related papers  (2024-09-30T07:01:21Z)
• Speech Recognition Rescoring with Large Speech-Text Foundation Models [20.145389016219106]
  Large language models (LLM) have demonstrated the ability to understand human language by leveraging large amount of text data. Automatic speech recognition (ASR) systems are often limited by available transcribed speech data. Recent multi-modal large language models have demonstrated strong spoken language understanding.
  arXiv  Detail & Related papers  (2024-09-25T06:17:23Z)
• Enhancing Multilingual Speech Generation and Recognition Abilities in LLMs with Constructed Code-switched Data [30.966072545451183]
  We propose a MutltiLingual MultiTask (MLMT) model, integrating multilingual speech generation and recognition tasks within the single LLM. We develop an effective data construction approach that splits and equips words from different languages to equip synthesiss with CS ability without relying on CS data.
  arXiv  Detail & Related papers  (2024-09-17T08:11:07Z)
• Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions [68.98811048970963]
  We present a pioneering effort to investigate the capability of large language models (LLMs) in transcribing speech in multi-talker environments.<n>We use WavLM and Whisper encoder to extract multi-faceted speech representations that are sensitive to speaker characteristics and semantic context.<n>Experiments reveal the promising performance of our proposed system, MT-LLM, in cocktail party scenarios.
  arXiv  Detail & Related papers  (2024-09-13T07:28:28Z)
• SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks [94.10497337235083]
  We are first to explore the potential of prompting speech LMs in the domain of speech processing. We reformulate speech processing tasks into speech-to-unit generation tasks. We show that the prompting method can achieve competitive performance compared to the strong fine-tuning method.
  arXiv  Detail & Related papers  (2024-08-23T13:00:10Z)
• An End-to-End Speech Summarization Using Large Language Model [7.562198375754054]
  Speech Summarization (SSum) aims to generate human-like text summaries from spoken content. Research on large language models (LLMs) and multimodal information fusion has provided new insights. We propose an end-to-end SSum model that utilizes Q-Former as a connector for the audio-text modality.
  arXiv  Detail & Related papers  (2024-07-02T07:22:57Z)
• Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks [61.3055230762097]
  We propose a decoder-only language model, VoxtLM, that can perform four tasks: speech recognition, speech synthesis, text generation, and speech continuation. VoxtLM integrates text vocabulary with discrete speech tokens from self-supervised speech features and uses special tokens to enable multitask learning.
  arXiv  Detail & Related papers  (2023-09-14T03:13:18Z)
• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [70.08842857515141]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD.<n>Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)

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.