PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Abstract
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) systems: effective pronunciation modeling and robust homophone discrimination. Both are essential for raw or long-tail word recognition. The proposed approach adopts a two-stage learning paradigm. First, we introduce a pronunciation-guided context learning method. It employs an interleaved grapheme-phoneme context modeling strategy that incorporates grapheme-only distractors, encouraging the model to leverage phonemic cues for accurate recognition. Then, we propose a pronunciation-discriminative reinforcement learning method with perturbed label sampling to further enhance the model\'s ability to distinguish contextualized homophones. Experimental results on the public English Librispeech and Mandarin AISHELL-1 datasets indicate that PAC: (1) reduces relative Word Error Rate (WER) by 30.2% and 53.8% compared to pre-trained LLM-based ASR models, and (2) achieves 31.8% and 60.5% relative reductions in biased WER for long-tail words compared to strong baselines, respectively.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the PAC framework, which enhances ASR systems by integrating pronunciation-aware contextual modeling through a novel two-stage learning approach. This work represents a significant advancement in the field of speech recognition, particularly for challenging long-tail word recognition and homophone discrimination.
Methodology
The proposed PAC framework introduces a two-stage learning paradigm that effectively combines graphemic and phonemic information to enhance ASR performance. The first stage, Pronunciation-Guided Context Learning (PGCL), innovatively interleaves grapheme and phoneme contexts while incorporating distractor words to improve the model's sensitivity to pronunciation cues. The second stage, Pronunciation-Discriminative Reinforcement Learning (PDRL), employs perturbed label sampling to strengthen the model's ability to discriminate between homophones. This dual approach is both novel and practical, addressing critical limitations in existing ASR systems.
Results & evaluation
The experimental setup is robust, utilizing well-established datasets (Librispeech and AISHELL-1) for both English and Mandarin, which adds credibility to the findings. The reported improvements in Word Error Rate (WER) and biased WER (B-WER) are significant, with reductions of up to 60.5% for long-tail words, showcasing the effectiveness of the PAC framework. The ablation studies further validate the contributions of each component of the methodology, indicating thorough experimentation.
Reproducibility
The paper mentions that the code and models will be released upon publication, which is a positive aspect for reproducibility. However, specific implementation details regarding the training process, hyperparameters, and model architecture could be more thoroughly documented to enhance reproducibility.
Limitations
While the proposed method shows significant improvements, it may still struggle with extremely rare words or highly context-dependent phrases that are not well represented in the training data. The reliance on specific datasets may also limit the generalizability of the approach to other languages or dialects not covered in the experiments.
Broader impact
The PAC framework has the potential to significantly improve ASR systems, particularly in applications requiring accurate recognition of proper nouns and rare words, such as voice assistants, transcription services, and accessibility tools. Its implications extend to multilingual and code-switching scenarios, which are increasingly relevant in today's diverse linguistic landscape. The main contribution of this paper is the introduction of the PAC framework, which enhances ASR systems by integrating pronunciation-aware contextual modeling through a novel two-stage learning approach. This work represents a significant advancement in the field of speech recognition, particularly for challenging long-tail word recognition and homophone discrimination.
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