Diffusion Language Models for Speech Recognition
Summary: arXiv:2604.14001v1 Announce Type: cross
In recent years, diffusion language models have surfaced as a transformative alternative to traditional language models. Their unique capabilities, particularly in bidirectional attention and parallel text generation, have made them an attractive option for various natural language processing tasks. This article delves into the potential applications of these models in the realm of speech recognition, highlighting their efficacy and the methodologies we have developed for their integration.
Introduction to Diffusion Language Models
Diffusion language models (DLMs) are a class of models that leverage diffusion processes to generate text. Unlike conventional models that typically rely on unidirectional structures, DLMs facilitate bidirectional attention, allowing them to consider context from both preceding and succeeding text. This capability is particularly beneficial in speech recognition, where understanding context can significantly enhance accuracy.
Incorporating Masked Diffusion Language Models
Our research introduces a comprehensive guide on how to effectively incorporate masked diffusion language models (MDLM) into automatic speech recognition (ASR) systems. The primary focus is on rescoring hypotheses generated by ASR, which often suffer from inaccuracies due to various factors such as noise, accents, or homophones.
Uniform-State Diffusion Models
In addition to MDLMs, we explore the implementation of uniform-state diffusion models (USDMs). These models offer a novel approach to processing ASR outputs by integrating different layers of information. They provide a different perspective on the probability distributions associated with recognized text, enabling more accurate rescoring of ASR hypotheses.
Joint-Decoding Methodology
One of the significant advancements in our study is the introduction of a new joint-decoding method. This method synergizes the strengths of Connectionist Temporal Classification (CTC) and USDM by merging their respective probability distributions at each decoding step. The process is as follows:
- CTC generates framewise probability distributions that reflect the likelihood of phonemes in the audio signal.
- USDM computes labelwise probability distributions that encapsulate strong language knowledge.
- By integrating these two distributions, our method produces new text candidates that leverage both acoustic and linguistic information.
Results and Findings
Our empirical findings indicate that both USDM and MDLM significantly enhance the accuracy of recognized text. The integration of these models not only improves the performance of ASR systems but also demonstrates the potential for further advancements in the field of speech recognition. We are excited to share that all our code and recipes are publicly available, encouraging further exploration and development by the research community.
Conclusion
In conclusion, diffusion language models represent a promising frontier in speech recognition technology. By leveraging the capabilities of MDLM and USDM, we can enhance the accuracy and efficiency of ASR systems, paving the way for more robust applications in real-world scenarios. We invite researchers and practitioners alike to explore our findings and contribute to this rapidly evolving field.