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Posts

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publications

WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement

Published in IEEE Signal Processing Letters, 2020

Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, the local and sequential properties of speech should be efficiently taken into account when modelling. However, in most current E2E models for SE, these properties are either not fully considered or are too complex to be realized. In this letter, we propose an efficient E2E SE model, termedWaveCRN. Compared with models based on convolutional neural networks (CNN) or long short-term memory (LSTM), WaveCRN uses a CNN module to capture the speech locality features and a stacked simple recurrent units (SRU) module to model the sequential property of the locality features. Different from conventional recurrent neural networks and LSTM, SRU can be efficiently parallelized in calculation, with even fewer model parameters. In order to more effectively suppress noise components in the noisy speech, we derive a novel restricted feature masking approach, which performs enhancement on the feature maps in the hidden layers; this is different from the approaches that apply the estimated ratio mask to the noisy spectral features, which is commonly used in speech separation methods. Experimental results on speech denoising and compressed speech restoration tasks confirm that with the SRU and the restricted feature map, WaveCRN performs comparably to other state-of-the-art approaches with notably reduced model complexity and inference time.

Recommended citation: T. -A. Hsieh, H. -M. Wang, X. Lu and Y. Tsao, "WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-End Speech Enhancement," in IEEE Signal Processing Letters, vol. 27, pp. 2149-2153, 2020. https://ieeexplore.ieee.org/iel7/97/8966529/09272838.pdf

Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing

Published in APSIPA ASC, 2020

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 𝐿1 pre-trained Transformer is fine-tuned using a MetricGAN framework. The proposed MetricGAN can be treated as a general postprocessing 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.

Recommended citation: S.-W. Fu, C.-F. Liao, T.-A. Hsieh, K.-H. Hung, S.-S. Wang, C. Yu, H.-C. Kuo, R. E Zezario, Y.-J. Li, S.-Y. Chuang, Y.-J. Lu, Y.-C. Lin, Y. Tsao, "Boosting Objective Scores of a Speech Enhancement Model by MetricGAN Post-processing,” in Proc. APSIPA ASC, 2020. https://arxiv.org/pdf/2006.10296.pdf

Improving Perceptual Quality by Phone-Fortified Perceptual Loss using Wasserstein Distance for Speech Enhancement

Published in Proc. Interspeech, 2021

Speech enhancement (SE) aims to improve speech quality and intelligibility, which are both related to a smooth transition in speech segments that may carry linguistic information, e.g. phones and syllables. In this study, we propose a novel phone-fortified perceptual loss (PFPL) that takes phonetic information into account for training SE models. To effectively incorporate the phonetic information, the PFPL is computed based on latent representations of the wav2vec model, a powerful self-supervised encoder that renders rich phonetic information. To more accurately measure the distribution distances of the latent representations, the PFPL adopts the Wasserstein distance as the distance measure. Our experimental results first reveal that the PFPL is more correlated with the perceptual evaluation metrics, as compared to signal-level losses. Moreover, the results showed that the PFPL can enable a deep complex U-Net SE model to achieve highly competitive performance in terms of standardized quality and intelligibility evaluations on the Voice Bank–DEMAND dataset.

Recommended citation: T.-A. Hsieh and C. Yu and S.-W. Fu and X. Lu and Y. Tsao, “Improving Perceptual Quality by Phone-Fortified Perceptual Loss Using Wasserstein Distance for Speech Enhancement,” in Proc. Interspeech, 2021. https://www.isca-speech.org/archive/pdfs/interspeech_2021/hsieh21_interspeech.pdf

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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