Phonemes vs. Projectors: An Investigation of Speech-Language Interfaces for LLM-based ASR
Abstract
Integrating pretrained speech encoders with large language models (LLMs) is promising for ASR, but performance and data efficiency depend on the speech-language interface. A common choice is a learned projector that maps encoder features into the LLM embedding space, whereas an alternative is to expose discrete phoneme sequences to the LLM. Using the same encoder and LLM backbones, we compare phoneme-based and vanilla projector-based interfaces in high-resource English and low-resource Tatar. We also propose a BPE-phoneme interface that groups frequent local phoneme patterns while preserving explicit word-boundary cues for phoneme-to-grapheme generation. On LibriSpeech, the phoneme-based interface is competitive with the vanilla projector, and the BPE-phoneme interface yields further gains. On Tatar, the phoneme-based interface substantially outperforms the vanilla projector. We further find that phoneme supervision yields a phoneme-informed hybrid interface that is stronger than the vanilla projector.
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 comparative analysis of phoneme-based and projector-based interfaces for LLM-integrated ASR, revealing that phoneme-based approaches can significantly enhance performance, especially in low-resource settings. This work advances the understanding of speech-language interfaces and provides a foundation for future innovations in ASR systems.
Methodology
The paper presents a systematic comparison of two speech-language interfaces—projector-based and phoneme-based—for integrating large language models (LLMs) with automatic speech recognition (ASR). The methodology is robust, utilizing controlled backbones and extensive experiments across high-resource (LibriSpeech) and low-resource (Tatar) settings. The introduction of a BPE-phoneme interface is particularly innovative, as it combines phoneme sequences with boundary-awareness, enhancing the model's performance. The two-stage training process for both interfaces is well-defined and leverages phoneme supervision effectively.
Results & evaluation
The experiments are comprehensive, covering a variety of configurations and datasets. The results demonstrate that the phoneme-based interface significantly outperforms the vanilla projector, especially in low-resource scenarios, which is a critical finding for the field. The paper provides clear metrics (Word Error Rate - WER) and contextualizes results against recent baselines, showcasing the competitive performance of the proposed methods.
Reproducibility
The paper outlines the experimental setup, including details on datasets, model architectures, and training procedures. However, the absence of a public repository or demo limits reproducibility. The authors mention the use of specific models and configurations but do not provide code or data access, which is essential for full reproducibility in machine learning research.
Limitations
One limitation is the lack of exploration into the scalability of the proposed interfaces beyond the tested languages and datasets. Additionally, while the BPE-phoneme interface shows promise, its effectiveness in other languages or dialects has not been evaluated. The paper also does not address potential biases in the datasets used, which could impact generalizability.
Broader impact
The findings have significant implications for ASR systems, particularly in low-resource languages, where traditional methods often struggle. The insights gained from this study could inform future research and development in multilingual ASR, potentially leading to more inclusive and accessible speech technologies. The main contribution of this paper is the comparative analysis of phoneme-based and projector-based interfaces for LLM-integrated ASR, revealing that phoneme-based approaches can significantly enhance performance, especially in low-resource settings. This work advances the understanding of speech-language interfaces and provides a foundation for future innovations in ASR systems.
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