Boosting Objective Scores Of A Speech Enhancement Model By Metricgan Post-processing
2020 · Szu-Wei Fu, Chien-Feng Liao, Tsun-An Hsieh, et al.
Abstract
The Transformer architecture has demonstrated a superior ability compared to recurrent neural networks in many different natural language processing applications. Therefore, our study applies a modified Transformer in a speech enhancement task. Specifically, positional encoding in the Transformer may not be necessary for speech enhancement, and hence, it is replaced by convolutional layers. To further improve the perceptual evaluation of the speech quality (PESQ) scores of enhanced speech, the L_1 pre-trained Transformer is fine-tuned using a MetricGAN framework. The proposed MetricGAN can be treated as a general post-processing module to further boost the objective scores of interest. The experiments were conducted using the data sets provided by the organizer of the Deep Noise Suppression (DNS) challenge. Experimental results demonstrated that the proposed system outperformed the challenge baseline, in both subjective and objective evaluations, with a large margin.
Authors
(none)
Tags
Stats
Related papers
- Metricgan: Generative Adversarial Networks Based Black-box Metric Scores Optimization For Speech Enhancement (2019)0.00
- CMGAN: Conformer-based Metric GAN For Speech Enhancement (2022)15.13
- Multi-cmgan+/+: Leveraging Multi-objective Speech Quality Metric Prediction For Speech Enhancement (2023)0.00
- Postgan: A Gan-based Post-processor To Enhance The Quality Of Coded Speech (2022)9.76
- Multi-metric Optimization Using Generative Adversarial Networks For Near-end Speech Intelligibility Enhancement (2021)8.60
- Study Of Lightweight Transformer Architectures For Single-channel Speech Enhancement (2025)3.58
- Metricgan-u: Unsupervised Speech Enhancement/ Dereverberation Based Only On Noisy/ Reverberated Speech (2021)11.67
- Imetricgan: Intelligibility Enhancement For Speech-in-noise Using Generative Adversarial Network-based Metric Learning (2020)9.41