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arXiv · 2509.04473

SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings

Jaekwon Yoo·Kunal Chandiramani·Divya Tadimeti·Abenezer Girma·Chandra Dhir
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Abstract

While integrating speech encoder with LLM requires substantial data and resources, use cases face limitations due to insufficient availability. To address this, we propose a solution with a parameter-efficient adapter that converts speech embeddings into LLM-compatible tokens, focusing on end-to-end automatic speech recognition (ASR), named entity recognition (NER), and sentiment analysis (SA). To reduce labeling costs, we employ an LLM-based synthetic dataset annotation technique. The proposed adapter, using 7x fewer trainable parameters, achieves significant performance gains: a 26% relative Word Error Rates (WER) improvement on the LibriSpeech ASR task, a 6.3% relative F1 score increase on the NER task, and a 32% relative F1 score boost on the SA task. Moreover, using advanced techniques such as adding a classifier regularizer and optimizing the LLM with Low-Rank Adaptation (LoRA) yields notable performance gains, with Spoken Language Understanding Evaluation (SLUE) score improvement of 6.6% and 9.5%

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Jaekwon Yoo, Kunal Chandiramani, Divya Tadimeti, Abenezer Girma, Chandra Dhir. 2025-08-29. SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings. https://arxiv.org/abs/2509.04473

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