The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans (2012.13006v1)

Abstract: This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to mainly deal with end-to-end speech recognition experiments based on sequence-to-sequence modeling. The project has grown rapidly and now covers a wide range of speech processing applications. Now ESPnet also includes text to speech (TTS), voice conversation (VC), speech translation (ST), and speech enhancement (SE) with support for beamforming, speech separation, denoising, and dereverberation. All applications are trained in an end-to-end manner, thanks to the generic sequence to sequence modeling properties, and they can be further integrated and jointly optimized. Also, ESPnet provides reproducible all-in-one recipes for these applications with state-of-the-art performance in various benchmarks by incorporating transformer, advanced data augmentation, and conformer. This project aims to provide up-to-date speech processing experience to the community so that researchers in academia and various industry scales can develop their technologies collaboratively.

The 2020 ESPnet Update: Progress in End-to-End Speech Processing

The 2020 update of ESPnet presents significant advancements in the open-source, end-to-end speech processing toolkit, initially developed to facilitate sequence-to-sequence modeling in automatic speech recognition (ASR). This paper documents the expansion of ESPnet's scope to encompass text-to-speech (TTS), voice conversion (VC), speech translation (ST), and speech enhancement (SE). All applications benefit from end-to-end training, leveraging the capabilities of modern deep learning architectures and advanced data augmentation techniques.

Key Developments

Broadened Applications:

ESPnet's functionality now extends beyond ASR to include TTS, VC, ST, and SE. The incorporation of these applications emphasizes ESPnet's versatility and adaptability in addressing a wide range of speech processing tasks. For example, ESPnet-TTS integrates ASR/TTS joint training, significantly enhancing performance and versatility in generating speech from text and vice versa.

Notable Architectures and Methods:

ESPnet has incorporated state-of-the-art neural architectures such as Transformers and Conformers, which have improved accuracy across various speech processing tasks. The Conformer architecture, in particular, has enhanced local pattern recognition while maintaining the global context captured by Transformers.

ESPnet2 Training System:

A restructuring of the training framework, known as ESPnet2, has facilitated enhancements in distributed training and efficient memory utilization. This new system has standardized training across tasks like ASR and TTS, allowing for more seamless integration and optimization.

Numerical Results

The ESPnet update reports substantial reductions in character and word error rates (CER/WER) across major ASR datasets, attributable to novel architecture implementations. Specific improvements such as a WER of 4.9% on the LibriSpeech test demonstrate the toolkit's capacity to yield competitive results with modern methods like Conformers and advanced data augmentation.

Implications and Future Directions

The developments in ESPnet have practical applications in areas requiring robust end-to-end speech processing solutions, such as real-time translation, enhanced voice assistants, and improved communication devices. The toolkit's ability to incorporate new research advances quickly means it can continually offer cutting-edge solutions to speech processing challenges.

Theoretically, ESPnet's architecture allows for extending sequence-to-sequence modeling capabilities across diverse tasks, enabling innovative approaches such as non-autoregressive modeling and multi-speaker ASR. This adaptability positions ESPnet as a valuable resource in both academic research and industry development.

Looking forward, the ESPnet project aims to enhance online and streaming functionalities, develop speech-to-speech translation capabilities, and explore comprehensive speech conversation understanding systems. By focusing on these areas, ESPnet seeks to remain at the forefront of speech technology research and application, ensuring its relevance in the evolving landscape of artificial intelligence.