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arXiv · 2502.09284

SparQLe: Speech Queries to Text Translation Through LLMs

Amirbek Djanibekov·Hanan Aldarmaki
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Abstract

With the growing influence of Large Language Models (LLMs), there is increasing interest in integrating speech representations with them to enable more seamless multi-modal processing and speech understanding. This study introduces a novel approach that combines self-supervised speech representations with instruction-tuned LLMs for speech-to-text translation. The proposed approach leverages a modality adapter to align extracted speech features with instruction-tuned LLMs using English speech data. 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, offering a promising approach for various speech understanding applications.

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Amirbek Djanibekov, Hanan Aldarmaki. 2025-02-13. SparQLe: Speech Queries to Text Translation Through LLMs. https://arxiv.org/abs/2502.09284

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