Generating Training Targets for Real-World Speech Enhancement via Close-to-Distant Microphone Projection
Abstract
Training neural networks (NNs) for speech enhancement (SE) in distant speech-capturing scenarios requires paired distorted and clean reference speech signals. While such data are often generated through simulation, the mismatch between simulated and real recordings significantly limits SE accuracy. To address this issue, we propose Close-to-Distant microphone Projection (C2D projection), a method that generates paired data from real recordings captured by close and distant microphones. C2D projection estimates an optimal projection matrix that transforms close-microphone inputs into clean reference signals aligned with distant-microphone recordings, while simultaneously performing denoising. We show this projection can be effectively realized using a variant of the Parametric Multichannel Wiener Filter (PMWF). Experimental results demonstrate that an NN trained with C2D-projected data outperforms the state-of-the-art Guided Source Separation (GSS) on the challenging CHiME6 dinner party ASR task under oracle diarization, when using the enhanced output from GSS as an auxiliary input to the NN.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach to generating training targets for speech enhancement in real-world scenarios, significantly improving upon existing methods. The technical contributions, particularly in the formulation of the C2D projection method and its empirical validation, highlight its potential impact on the field of audio processing and machine learning.
Methodology
The proposed Close-to-Distant microphone Projection (C2D projection) method is a significant advancement in generating training targets for speech enhancement in real-world scenarios. The methodology effectively addresses the challenge of obtaining paired clean and distorted speech signals by leveraging recordings from close and distant microphones. The use of a projection matrix derived from a variant of the Parametric Multichannel Wiener Filter (PMWF) is innovative, as it allows for simultaneous denoising and alignment of signals, which is crucial for training neural networks. The paper provides a clear mathematical formulation and rationale for the method, making it accessible for replication and further research.
Results & evaluation
The experimental evaluation is robust, utilizing the CHiME6 and CHiME8 datasets, which are well-regarded benchmarks in the field of speech enhancement and automatic speech recognition. The results demonstrate that the C2D projection method outperforms the state-of-the-art Guided Source Separation (GSS) approach under both matched and mismatched conditions. The use of objective metrics such as tcpWER and DNSMOS adds credibility to the findings. However, the paper could benefit from additional qualitative assessments or user studies to further validate the improvements in speech intelligibility and quality.
Reproducibility
The paper includes sufficient detail regarding the implementation of the C2D projection method and the training of the neural network, referencing publicly available code for the model. However, the reproducibility could be enhanced by providing more explicit details on the training process, hyperparameters, and the specific configurations used for the experiments, as well as making the generated datasets available.
Limitations
One limitation noted is the reliance on oracle diarization labels during training, which may not be feasible in practical applications. Additionally, while the method shows robustness against some mismatches in training and test conditions, there are scenarios where performance degradation is observed, indicating that further work is needed to enhance the method's adaptability to diverse environments.
Broader impact
The C2D projection method has significant implications for real-world applications in speech enhancement, particularly in environments where distant microphones are used, such as in meetings or public speaking events. The ability to generate high-quality training targets from real recordings can lead to improved performance in automatic speech recognition systems, potentially enhancing user experiences in various audio-based applications. The findings could also inspire further research into novel training techniques for other audio processing tasks. The paper presents a novel approach to generating training targets for speech enhancement in real-world scenarios, significantly improving upon existing methods. The technical contributions, particularly in the formulation of the C2D projection method and its empirical validation, highlight its potential impact on the field of audio processing and machine learning.
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