Equipping LLM with Directional Multi-Talker Speech Understanding Capabilities

• URL: http://arxiv.org/abs/2602.07211v1
• Date: Fri, 06 Feb 2026 21:45:48 GMT
• Title: Equipping LLM with Directional Multi-Talker Speech Understanding Capabilities
• Authors: Ju Lin, Jing Pan, Ruizhi Li, Ming Sun, Yuzong Liu, Alaa Hassan, Jing Zheng, Florian Metze,
• Abstract summary: We propose two novel approaches to integrate directivity into large language models (LLM)<n>All of the approaches utilize a multi-microphone array embedded in smart glasses to optimize directivity interpretation and processing in a streaming manner.
• Score: 20.51281468416298
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent studies have demonstrated that prompting large language models (LLM) with audio encodings enables effective speech understanding capabilities. However, most speech LLMs are trained on single-channel, single-talker data, which makes it challenging to directly apply them to multi-talker and multi-channel speech understanding task. In this work, we present a comprehensive investigation on how to enable directional multi-talker speech understanding capabilities for LLMs, specifically in smart glasses usecase. We propose two novel approaches to integrate directivity into LLMs: (1) a cascaded system that leverages a source separation front-end module, and (2) an end-to-end system that utilizes serialized output training. All of the approaches utilize a multi-microphone array embedded in smart glasses to optimize directivity interpretation and processing in a streaming manner. Experimental results demonstrate the efficacy of our proposed methods in endowing LLMs with directional speech understanding capabilities, achieving strong performance in both speech recognition and speech translation tasks.

Related papers

• EmoSLLM: Parameter-Efficient Adaptation of LLMs for Speech Emotion Recognition [0.0]
  Emotion recognition from speech is a challenging task that requires capturing both linguistic and paralinguistic cues.<n>Recent works have highlighted the ability of Large Language Models (LLMs) to perform tasks outside of the sole natural language area.<n>This work proposes a novel approach that fine-tunes an LLM with audio and text representations for emotion prediction.
  arXiv  Detail & Related papers  (2025-08-19T06:58:16Z)
• SparQLe: Speech Queries to Text Translation Through LLMs [0.8901073744693314]
  This study introduces a novel approach that combines self-supervised speech representations with instruction-tuned LLMs for speech-to-text translation.<n>Our experiments demonstrate that this method effectively preserves the semantic content of the input speech and serves as an effective bridge between self-supervised speech models and instruction-tuned LLMs.
  arXiv  Detail & Related papers  (2025-02-13T12:57:15Z)
• LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation [72.02635550088546]
  This work explores how large language models (LLMs) can enhance CLIP's capability, especially for processing longer and more complex image captions.<n>We introduce a caption-to-caption contrastive fine-tuning framework, significantly enhancing the discriminative quality of LLM outputs.<n>Our approach outperforms LoRA-based methods, achieving nearly fourfold faster training with superior performance.
  arXiv  Detail & Related papers  (2024-11-07T18:59:16Z)
• 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)
• Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM [1.3089936156875277]
  We introduce a speech-conditioned Large Language Model (LLM) integrated with a Mixture of Experts (MoE) based connector. We propose an Insertion and Deletion of Interruption Token (IDIT) mechanism for better transfer text generation ability of LLM to speech recognition task. We also present a connecter with MoE architecture that manages multiple languages efficiently.
  arXiv  Detail & Related papers  (2024-09-24T09:20:22Z)
• Large Language Models are Strong Audio-Visual Speech Recognition Learners [53.142635674428874]
  Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities.<n>We propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities.<n>We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.79% and 0.77%, respectively.
  arXiv  Detail & Related papers  (2024-09-18T21:17:27Z)
• 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)
• Investigating Decoder-only Large Language Models for Speech-to-text Translation [39.17113782374464]
  Large language models (LLMs) are known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains. We propose a decoder-only architecture that enables the LLM to directly consume the encoded speech representation and generate the text translation. Our model achieves state-of-the-art performance on CoVoST 2 and FLEURS among models trained without proprietary data.
  arXiv  Detail & Related papers  (2024-07-03T14:42:49Z)
• Prompting Large Language Models with Audio for General-Purpose Speech Summarization [13.415189715216354]
  We introduce a framework for speech summarization that leverages the processing and reasoning capabilities of large language models (LLMs) We propose an end-to-end system that combines an instruction-tuned LLM with an audio encoder that converts speech into token representations that the LLM can interpret.
  arXiv  Detail & Related papers  (2024-06-10T02:04:28Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• Boosting Large Language Model for Speech Synthesis: An Empirical Study [86.89548753080432]
  Large language models (LLMs) have made significant advancements in natural language processing and are concurrently extending the language ability to other modalities, such as speech and vision. We conduct a comprehensive empirical exploration of boosting LLMs with the ability to generate speech, by combining pre-trained LLM LLaMA/OPT and text-to-speech synthesis model VALL-E. We compare three integration methods between LLMs and speech models, including directly fine-tuned LLMs, superposed layers of LLMs and VALL-E, and coupled LLMs and VALL-E using LLMs as a powerful text encoder
  arXiv  Detail & Related papers  (2023-12-30T14:20:04Z)

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.