Improving Design of Input Condition Invariant Speech Enhancement (2401.14271v2)

Abstract: Building a single universal speech enhancement (SE) system that can handle arbitrary input is a demanded but underexplored research topic. Towards this ultimate goal, one direction is to build a single model that handles diverse audio duration, sampling frequencies, and microphone variations in noisy and reverberant scenarios, which we define here as "input condition invariant SE". Such a model was recently proposed showing promising performance; however, its multi-channel performance degraded severely in real conditions. In this paper we propose novel architectures to improve the input condition invariant SE model so that performance in simulated conditions remains competitive while real condition degradation is much mitigated. For this purpose, we redesign the key components that comprise such a system. First, we identify that the channel-modeling module's generalization to unseen scenarios can be sub-optimal and redesign this module. We further introduce a two-stage training strategy to enhance training efficiency. Second, we propose two novel dual-path time-frequency blocks, demonstrating superior performance with fewer parameters and computational costs compared to the existing method. All proposals combined, experiments on various public datasets validate the efficacy of the proposed model, with significantly improved performance on real conditions. Recipe with full model details is released at https://github.com/espnet/espnet.

• The paper presents a redesigned channel modeling module and dual-path time-frequency blocks that significantly improve performance under diverse input conditions.
• It employs a decoupled processing strategy with a two-stage training process that enhances model adaptability, achieving nearly 90% improvements in DNSMOS scores and lower WER.
• The research delivers a resource-efficient universal speech enhancement system that maintains competitive performance in both simulated and real-world environments.

Improving Design of Input Condition Invariant Speech Enhancement

The paper "Improving Design of Input Condition Invariant Speech Enhancement" addresses the challenges in constructing a universal speech enhancement (SE) system capable of handling diverse input conditions. It is an extension of prior research in the field of deep learning-based SE that has predominantly focused on static input conditions.

Overview

Central to the paper is the notion of "input condition invariant SE," which acknowledges the need for a model that competently manages various audio samples with differing durations, sampling rates, and microphone configurations. The objective is to create a robust SE model that can deliver high performance across a wide range of acoustic scenarios, both simulated and real.

Key Contributions

The authors present several advancements over existing models, such as the recently proposed Unconstrained Speech Enhancement System (USES):

1. Redesign of Channel-Modeling Module: Prior models demonstrated poor generalization in unseen conditions, primarily due to suboptimal channel modeling. The new design, termed TA$_{\text{tt}}$C, improves the model's ability to manage channels with disparate signal-to-noise ratios (SNRs).
2. Novel Dual-Path Time-Frequency Blocks: These blocks, implemented as USES2-Swin and USES2-Comp architectures, offer superior performance by incorporating local time-frequency joint modeling inspired by Swin transformer designs.
3. Decoupled Processing Strategy: The authors propose separating single- and multi-channel optimization efforts, thus enhancing the ability to fine-tune the model according to specific conditions. This design approach allows for better independence in adapting to various audio configurations without compromising performance.
4. Two-Stage Training Process: By introducing a training regimen that initially focuses on single-channel data and subsequently integrates multi-channel information, the model efficiency is significantly improved. This also acts to mitigate data balancing issues encountered in previous attempts.

Impactful Results

The efficacy of the proposed improvements is evident in their experimental validations. The authors report:

• Significant enhancements in multi-channel processing under real-world conditions, notably achieving nearly a 90% relative improvement in DNSMOS scores and word error rates (WER) on the CHiME-4 dataset.
• The USES2 models demonstrated a consistent performance in simulated conditions, maintaining benchmarks set by conventional models while excelling in real settings.
• Reduced computational complexity and parameter count, making these solutions not only effective but also resource-efficient.

Implications

From a practical standpoint, these improvements can foster the development of more adaptive and universal SE applications crucial in fields like telecommunications and automated speech recognition systems. Theoretically, this research provides a clear direction for further exploration of deep learning architectures tailored for varied and dynamic audio inputs.

Future Directions

The authors suggest future endeavors might incorporate handling a broader spectrum of distortions beyond noise and reverberation. This would advance the alignment of machine learning models with the complexity of real-world environments, potentially enriching both the scope and depth of universal SE systems.

In conclusion, the paper makes substantial strides in the domain of universal speech enhancement, providing robust solutions that exhibit significant promise for future exploration and application.