Speech Recognition: Large Language Models’ Breakthroughs

The field of automatic speech recognition (ASR) has taken a significant leap forward with the integration of large language models (LLMs). These powerful AI models have been shown to improve ASR performance by processing and analyzing vast amounts of data, including speech. Recent studies have utilized prefix-based LLMs, which directly apply speech as a prefix to LLMs for ASR, demonstrating a 12% relative improvement in WER over the baseline with fine-tuned LLMs.

Additionally, PrefixTuning and language-based soft prompting have been found to improve ASR performance without increasing model complexity or altering the inference pipeline, offering a lightweight alternative to fine-tuning. As research continues to explore the applicability of these techniques to other tasks, we can expect significant improvements in various prediction tasks, including ASR.

Large language models (LLMs) have revolutionized automatic speech recognition (ASR) research by addressing various types of prediction errors. However, despite their advancements, LLMs still suffer from drawbacks such as higher insertions and code-switching errors. In this context, researchers have explored the use of speech prefixes to improve LLM performance.

The concept of prefix-based models has gained significant attention in recent years. PrefixLM is an LLM variant where the input text is accompanied by a prefix that can take the form of text, speech, or image. This prefix provides additional context for the model, enabling it to predict text autoregressively and mimicking an end-to-end ASR model.

Previous work has demonstrated that LLM performance improves with better speech encodings or prefix tokens extracted from self-supervised and supervised models. Scaling the speech encoder also enhances the use of speech prefixes, further improving the recognition ability of LLM models such as LLaMA. PrefixLM with speech prefixes have been trained for multiple tasks, including speech recognition and speech translation.

One notable approach is prefixtuning, which offers a lightweight alternative to fine-tuning. This technique involves prepending a trainable token sequence to the text input, optimizing only the prefix-related parameters to adapt the model effectively to downstream tasks. Prefixtuning has been incorporated into image and video-based prefixLM models as well, demonstrating its versatility.

The use of speech prefixes in LLMs offers several benefits, including improved recognition performance and reduced complexity. By optimizing speech prefixes, researchers have found that ASR performance improves significantly. This approach does not increase model complexity or alter the inference pipeline, making it a simple yet effective solution.

Moreover, language-based soft prompting has been proposed to further improve LLM performance with frozen models. Empirical analysis on real-time test sets from 10 Indic languages demonstrates that speech prefixtuning yields improvements with both frozen and fine-tuned LLMs. The recognition results show a relative improvement of 12% in WER over the baseline with a fine-tuned LLM, while the proposed approach with a frozen LLM leads to a 31% relative improvement over basic soft-prompting prefixLM.

Speech prefixtuning is a simple yet effective approach that involves applying RNNT loss to perform speech prefixtuning. This technique optimizes speech prefixes, leading to better ASR performance without increasing model complexity or altering the inference pipeline. The proposed approach also incorporates language-based soft prompting to further improve LLM performance with frozen models.

The empirical analysis demonstrates that speech prefixtuning yields improvements with both frozen and fine-tuned LLMs. The recognition results show a significant improvement in WER over the baseline, highlighting the effectiveness of this approach. Furthermore, the use of RNNT loss enables the model to adapt effectively to downstream tasks without requiring additional fine-tuning.

The key findings of this research include:

• Speech prefixtuning yields improvements with both frozen and fine-tuned LLMs.
• The recognition results show a relative improvement of 12% in WER over the baseline with a fine-tuned LLM.
• The proposed approach with a frozen LLM leads to a 31% relative improvement over basic soft-prompting prefixLM.
• Language-based soft prompting further improves LLM performance with frozen models.

These findings demonstrate the effectiveness of speech prefixtuning and language-based soft prompting in improving LLM performance. The use of RNNT loss enables the model to adapt effectively to downstream tasks without requiring additional fine-tuning, making this approach a simple yet effective solution for improving ASR performance.

The implications of this research are significant, as they demonstrate the potential of speech prefixtuning and language-based soft prompting in improving LLM performance. The use of these techniques can lead to improved recognition accuracy, reduced complexity, and increased efficiency in ASR systems.

Moreover, the findings of this research have implications for various applications, including speech recognition, speech translation, and multilingual translation models. The proposed approach can be used to adapt multilingual translation models to bilingual speech translation tasks, further improving the recognition ability of LLMs.

The future directions of this research include exploring the use of speech prefixtuning and language-based soft prompting in other applications, such as image and video-based prefixLM models. Additionally, researchers can investigate the use of RNNT loss in other tasks, such as speech recognition and speech translation.

Furthermore, the development of more efficient and effective techniques for adapting LLMs to downstream tasks is an area of ongoing research. The proposed approach demonstrates the potential of using speech prefixes and language-based soft prompting to improve LLM performance, and further research can build upon these findings to develop even more effective solutions.

The limitations of this research include:

• The use of a limited dataset for empirical analysis.
• The need for further investigation into the effectiveness of speech prefixtuning and language-based soft prompting in other applications.
• The potential for overfitting or underfitting when using RNNT loss.

Despite these limitations, the findings of this research demonstrate the potential of speech prefixtuning and language-based soft prompting in improving LLM performance. Further research can address these limitations and explore new directions for improving ASR performance.