A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario

• URL: http://arxiv.org/abs/2412.00721v2
• Date: Wed, 04 Dec 2024 06:23:40 GMT
• Title: A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario
• Authors: Zheshu Song, Ziyang Ma, Yifan Yang, Jianheng Zhuo, Xie Chen,
• Abstract summary: Large Language Models (LLMs) have showcased exceptional performance across diverse NLP tasks. Their potential for addressing speech recognition challenges in low resource settings remains underexplored.
• Score: 9.290091297389033
• License:
• Abstract: Large Language Models (LLMs) have showcased exceptional performance across diverse NLP tasks, and their integration with speech encoder is rapidly emerging as a dominant trend in the Automatic Speech Recognition (ASR) field. Previous works mainly concentrated on leveraging LLMs for speech recognition in English and Chinese. However, their potential for addressing speech recognition challenges in low resource settings remains underexplored. Hence, in this work, we aim to explore the capability of LLMs in low resource ASR and Mandarin-English code switching ASR. We also evaluate and compare the recognition performance of LLM-based ASR systems against Whisper model. Extensive experiments demonstrate that LLM-based ASR yields a relative gain of 12.8\% over the Whisper model in low resource ASR while Whisper performs better in Mandarin-English code switching ASR. We hope that this study could shed light on ASR for low resource scenarios.

Related papers

• 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. We propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. 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 0.81% and 0.77%, respectively.
  arXiv  Detail & Related papers  (2024-09-18T21:17:27Z)
• Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages [51.12146889808824]
  Meta-Whisper is a novel approach to improve automatic speech recognition for low-resource languages. It enhances Whisper's ability to recognize speech in unfamiliar languages without extensive fine-tuning.
  arXiv  Detail & Related papers  (2024-09-16T16:04:16Z)
• LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation [15.520180125182756]
  Recent advancements in integrating speech information into large language models (LLMs) have significantly improved automatic speech recognition (ASR) accuracy. Existing methods often constrained by the capabilities of the speech encoders under varied acoustic conditions, such as accents. We propose LA-RAG, a novel Retrieval-Augmented Generation (RAG) paradigm for LLM-based ASR.
  arXiv  Detail & Related papers  (2024-09-13T07:28:47Z)
• SALSA: Speedy ASR-LLM Synchronous Aggregation [40.91241351045586]
  We propose SALSA, which couples the decoder layers of the ASR to the LLM decoder, while synchronously advancing both decoders. We evaluate SALSA on 8 low-resource languages in the FLEURS benchmark, yielding substantial WER reductions of up to 38%.
  arXiv  Detail & Related papers  (2024-08-29T14:00:57Z)
• Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models [21.85677682584916]
  speculative speech recognition (SSR) We propose a model which does SSR by combining a RNN-Transducer-based ASR system with an audio-ed language model (LM)
  arXiv  Detail & Related papers  (2024-07-05T16:52:55Z)
• An Embarrassingly Simple Approach for LLM with Strong ASR Capacity [56.30595787061546]
  We focus on solving one of the most important tasks in the field of speech processing, with speech foundation encoders and large language models (LLM) Recent works have complex designs such as compressing the output temporally for the speech encoder, tackling modal alignment for the projector, and utilizing parameter-efficient fine-tuning for the LLM. We found that delicate designs are not necessary, while an embarrassingly simple composition of off-the-shelf speech encoder, LLM, and the only trainable linear projector is competent for the ASR task.
  arXiv  Detail & Related papers  (2024-02-13T23:25:04Z)
• Large Language Models are Efficient Learners of Noise-Robust Speech Recognition [65.95847272465124]
  Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR) In this work, we extend the benchmark to noisy conditions and investigate if we can teach LLMs to perform denoising for GER. Experiments on various latest LLMs demonstrate our approach achieves a new breakthrough with up to 53.9% correction improvement in terms of word error rate.
  arXiv  Detail & Related papers  (2024-01-19T01:29:27Z)
• Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks [68.79880423713597]
  We introduce a method that utilizes the ASR system's lattice output instead of relying solely on the top hypothesis. Our in-context learning experiments, covering spoken question answering and intent classification, underline the LLM's resilience to noisy speech transcripts.
  arXiv  Detail & Related papers  (2024-01-05T17:58:10Z)
• Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study [0.0]
  This paper explores the integration of Large Language Models (LLMs) into Automatic Speech Recognition (ASR) systems. Our primary focus is to investigate the potential of using an LLM's in-context learning capabilities to enhance the performance of ASR systems.
  arXiv  Detail & Related papers  (2023-07-13T02:31:55Z)
• ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion [49.617722668505834]
  We show that our approach permits the application of speech synthesis and voice conversion to improve ASR systems using only one target-language speaker during model training. It is possible to obtain promising ASR training results with our data augmentation method using only a single real speaker in a target language.
  arXiv  Detail & Related papers  (2022-03-29T11:55:30Z)

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.