ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding (2207.09514v1)

Abstract: This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features have been added, including recent state-of-the-art speech enhancement models with their respective training and evaluation recipes. Importantly, a new interface has been designed to flexibly combine speech enhancement front-ends with other tasks, including automatic speech recognition (ASR), speech translation (ST), and spoken language understanding (SLU). To showcase such integration, we performed experiments on carefully designed synthetic datasets for noisy-reverberant multi-channel ST and SLU tasks, which can be used as benchmark corpora for future research. In addition to these new tasks, we also use CHiME-4 and WSJ0-2Mix to benchmark multi- and single-channel SE approaches. Results show that the integration of SE front-ends with back-end tasks is a promising research direction even for tasks besides ASR, especially in the multi-channel scenario. The code is available online at https://github.com/ESPnet/ESPnet. The multi-channel ST and SLU datasets, which are another contribution of this work, are released on HuggingFace.

• The paper presents a modular interface that integrates speech enhancement with ASR, ST, and SLU for improved word error rates.
• It incorporates state-of-the-art models like DCCRN, DC-CRN, and iNeuBe to support both single- and multi-channel processing.
• The toolkit expands datasets and adopts multi-task training objectives, enabling robust evaluations across diverse speech tasks.

ESPnet-SE++: Integrative Approaches in Speech Enhancement

The paper presented focuses on breakthroughs in the integration of speech separation and enhancement (SSE) within the ESPnet toolkit, culminating in the development of ESPnet-SE++. Notably, the authors introduce a series of features that improve upon the previous ESPnet-SE iteration, emphasizing state-of-the-art models and a new modular interface that combines speech enhancement front-ends with various downstream tasks such as automatic speech recognition (ASR), speech translation (ST), and spoken language understanding (SLU).

Key Contributions and Technical Advancements

ESPnet-SE++ is marked by several significant contributions:

1. Enhanced Modular Interface: The updated toolkit provides a seamless plug-and-play system enabling researchers to flexibly combine SSE models with back-end speech processing tasks. This integration is pivotal for research beyond traditional ASR, notably in multi-channel scenarios.
2. Incorporation of State-of-the-Art Models: The toolkit includes top-tier enhancement models such as DCCRN, DC-CRN, and iNeuBe, with extensive coverage from single-channel to multi-channel approaches, surpassing the capabilities of earlier iterations.
3. Comprehensive Dataset and Recipe Expansion: ESPnet-SE++ extends its corpus with new synthetic datasets and enhancement corpora. The newly designed datasets simulate noisy, reverberant environments, crucial for benchmarking SSE in advanced scenarios like distant speech processing.
4. Improved Training Objectives: The enhanced flexibility in training objectives allows for multi-task learning (MTL), enabling complex training frameworks to be established within a single platform.

Numerical Results and Performance Insights

The empirical evaluation illustrates that the novel integration within ESPnet-SE++ enhances performance across tasks. On the CHiME-4 corpus, the combination of enhancement and recognition models demonstrated significant improvements in word error rates—highlighting the synergy between SE and ASR systems. Additional experiments on the newly developed SLU and ST datasets, SLURP-S and LT-S, further underline ESPnet-SE++'s applicability across diverse task sets, with iNeuBe models offering impressive results.

Future Implications and Research Directions

The integration facilitated by ESPnet-SE++ displays immense promise for both academic research and practical applications. It opens avenues for robust speech understanding in smart devices and complex environments, where traditional models might falter. The potential for joint optimization of front- and back-end tasks suggests pathways for enhancements in efficiency and accuracy in multifaceted computational systems.

Future development could further explore unsupervised and generative techniques within this framework, capitalizing on robust model generalization to unseen noise conditions and diverse acoustical environments. Sustainable advancements in this field hold the potential to revolutionize our interaction with intelligent auditory systems, promoting adaptability and resilience in speech processing technology.