Speech Prefix-Tuning with RNNT Loss for Improving LLM Predictions (2406.14701v1)

Abstract: In this paper, we focus on addressing the constraints faced when applying LLMs to ASR. Recent works utilize prefixLM-type models, which directly apply speech as a prefix to LLMs for ASR. We have found that optimizing speech prefixes leads to better ASR performance and propose applying RNNT loss to perform speech prefix-tuning. This is a simple approach and does not increase the model complexity or alter the inference pipeline. We also propose language-based soft prompting to further improve with frozen LLMs. Empirical analysis on realtime testset from 10 Indic languages demonstrate that our proposed speech prefix-tuning yields improvements with both frozen and fine-tuned LLMs. Our recognition results on an average of 10 Indics show that the proposed prefix-tuning with RNNT loss results in a 12\% relative improvement in WER over the baseline with a fine-tuned LLM. Our proposed approches with the frozen LLM leads to a 31\% relative improvement over basic soft-prompting prefixLM.

An Examination of Speech Prefix-Tuning with RNNT Loss for Enhancing LLM Predictions

The paper "Speech Prefix-Tuning with RNNT Loss for Improving LLM Predictions" presents an exploration of optimizing the integration of LLMs with automatic speech recognition (ASR) systems, specifically via the use of speech prefixes. The paper focuses on enhancing the adaptability and performance of LLMs in ASR tasks without increasing model complexity or modifying the inference process.

Methodological Innovations

Central to this work is the introduction of speech prefix-tuning, leveraging the Recurrent Neural Network Transducer (RNNT) loss as a mechanism to improve ASR performance. This approach differs from traditional methods that tune with connectionist temporal classification (CTC) loss, highlighting RNNT's superior ability to handle autoregressive sequence-to-sequence tasks in multilingual contexts. Notably, the application of RNNT loss allows for an optimal alignment between speech and text without altering the computational model's complexity. This enhancement is particularly beneficial given the constraints often associated with fine-tuning large models.

Another significant contribution is the implementation of language-based soft prompting. This technique enables improved performance by optimally utilizing the context provided by speech inputs, particularly when working with frozen LLMs. By soft-prompting based on language identifiers (langID), the model can maintain a high level of accuracy across multiple languages without necessitating full model updates.

Empirical Results

The empirical analysis presented in the paper is based on a real-time dataset comprising 10 Indic languages. The introduction of RNNT loss for speech prefix-tuning results in a notable 12% relative improvement in word error rate (WER) when compared to baseline models utilizing a fine-tuned LLM. Furthermore, when operating with a frozen LLM, the system achieves a 31% relative improvement over basic soft-prompting models, demonstrating significant advancements in ASR accuracy.

The paper also contrasts the performance gains achieved through different tuning strategies, including prefix-tuning with CTC and RNNT losses. The results indicate that RNNT-based prefix-tuning outperforms its CTC counterpart, especially in handling complex multilingual data with extensive code-switching tendencies.

Implications and Future Prospects

The findings suggest that the strategic incorporation of RNNT loss into LLM-based ASR systems offers practical improvements without incurring the computational costs of more extensive fine-tuning processes. Furthermore, language-based soft prompting emerges as a valuable approach for optimizing multilingual ASR tasks, which could be critical in developing more robust, language-agnostic systems.

The research opens several avenues for future exploration, particularly in enhancing cross-modal token utilization in LLMs and extending the applicability of soft prompting to accommodate broader linguistic variations. The implications for practical applications are profound, particularly as speech recognition technologies become increasingly integrated into everyday digital interactions.

Furthermore, the minimalist approach to adjustment—targeting only specific parameters—heralds a shift towards more computationally-efficient ASR systems. In the evolving landscape of AI, such innovations are essential in maintaining the scalability and accessibility of advanced machine learning models.

In summary, this paper provides a compelling case for adopting speech prefix-tuning with RNNT loss, offering novel insights into the optimization of LLMs within ASR frameworks, and paving the way for future advancements in the field of multilingual speech recognition.