EURO: ESPnet Unsupervised ASR Open-source Toolkit (2211.17196v3)

Abstract: This paper describes the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an end-to-end open-source toolkit for unsupervised automatic speech recognition (UASR). EURO adopts the state-of-the-art UASR learning method introduced by the Wav2vec-U, originally implemented at FAIRSEQ, which leverages self-supervised speech representations and adversarial training. In addition to wav2vec2, EURO extends the functionality and promotes reproducibility for UASR tasks by integrating S3PRL and k2, resulting in flexible frontends from 27 self-supervised models and various graph-based decoding strategies. EURO is implemented in ESPnet and follows its unified pipeline to provide UASR recipes with a complete setup. This improves the pipeline's efficiency and allows EURO to be easily applied to existing datasets in ESPnet. Extensive experiments on three mainstream self-supervised models demonstrate the toolkit's effectiveness and achieve state-of-the-art UASR performance on TIMIT and LibriSpeech datasets. EURO will be publicly available at https://github.com/espnet/espnet, aiming to promote this exciting and emerging research area based on UASR through open-source activity.

• The paper introduces EURO, which enhances unsupervised ASR by integrating self-supervised learning, GAN training, and advanced k2-based decoding.
• The methodology incorporates 27 SSL models and parallel computing to streamline data preparation and reduce phoneme error rates.
• Experimental results on TIMIT and Librispeech demonstrate state-of-the-art performance, showcasing the toolkit's versatility in diverse linguistic settings.

EURO: ESPnet Unsupervised ASR Open-source Toolkit

The paper introduces the ESPnet Unsupervised ASR Open-source Toolkit (EURO), an open-source solution designed to advance the field of unsupervised automatic speech recognition (UASR). Building upon the foundational methodology of Wav2vec-U, the toolkit enhances the conventional unsupervised ASR paradigm by employing self-supervised learning (SSL) and adversarial training within a unified pipeline facilitated by ESPnet.

The toolkit augments the original framework by incorporating the S3PRL and k2 toolkits, providing a versatile frontend that features 27 self-supervised models alongside diverse graph-based decoding strategies. This integration facilitates the adaptation of multiple datasets while significantly streamlining the data preparation process through parallel computing capabilities.

Framework and Methodology

EURO maintains the core principles of Wav2vec-U but introduces additional features aimed at improving efficiency and reproducibility. The pipeline utilizes SSL models as feature extractors, followed by generative adversarial network (GAN) training, combined with auxiliary losses for model stability. These allow the model to navigate the myriad challenges associated with unsupervised learning, such as the lack of paired data.

A notable advancement is the k2-based decoding strategy that leverages weighted finite-state transducers (WFSTs), optimizing word-level recognition through an adaptable lattice-scoring design. This structure replaces traditional methods, which are often limited to phonemic transcriptions, expanding the applicability of the toolkit to various linguistic datasets.

Experimental Insights

Experiments conducted using both TIMIT and Librispeech datasets underscore EURO's efficacy. Notably, the toolkit achieves state-of-the-art results with different SSL models, including HuBERT and WavLM. For TIMIT, EURO improved performance relative to the baseline wav2vec-U, achieving a phoneme error rate (PER) reduction to 14.6%. In the Librispeech evaluations, EURO demonstrated strong results, with PER and WER comparisons favoring HuBERT and wav2vec 2.0 in challenging conditions.

Implications and Future Directions

EURO's contributions to UASR are multifaceted, impacting both practical applications and theoretical explorations. By fostering greater accessibility to UASR models, EURO encourages experimentation and benchmarking across diverse linguistic datasets, especially low-resource languages. The inclusion of multiple state-of-the-art SSL models affords researchers unparalleled flexibility in tackling unique speech recognition challenges.

Furthermore, the potential for future integrations with more advanced LLMs or alternative SSL methods indicates a promising trajectory for EURO. Continued developments could explore more efficient GAN training protocols or extended decoding strategies to further reduce computational overhead and improve linguistic accuracy.

Conclusion

EURO represents a significant step forward in open-source solutions for unsupervised ASR. By addressing reproducibility and offering extensive compatibility with diverse datasets and models, EURO sets a foundation for ongoing and future research in the field. The potential for evolving the toolkit to encompass broader applications in multilingual and low-resource settings remains a highly attractive prospect for the AI and speech processing communities.