https://github.com/JasonSWFu/Quality-Net

Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM. (Interspeech, 2018, with Travel Grants)
https://github.com/JasonSWFu/Quality-Net

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Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM. (Interspeech, 2018, with Travel Grants)

• Host: GitHub
• URL: https://github.com/JasonSWFu/Quality-Net
• Owner: JasonSWFu
• Created: 2019-07-22T18:22:12.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-22T18:46:26.000Z (over 5 years ago)
• Last Synced: 2024-08-02T07:13:43.157Z (6 months ago)
• Language: Python
• Homepage:
• Size: 91.8 KB
• Stars: 84
• Watchers: 2
• Forks: 16
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-speech-enhancement - [Quality-Net

README

        
# Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM (Interspeech 2018)

### Introduction

Herein, we propose a novel, end-to-end, and non-intrusive speech quality evaluation model, termed Quality-Net, based on bidirectional long short-term memory (BLSTM). In addition, to prevent Quality-Net from becoming an incomprehensible black box, its structure is designed to automatically learn (infer) a reasonable frame-level quality. This gives Quality-Net the ability to locate the degraded regions in an utterance. Although our ultimate goal is to learn the mapping function of the human listening perception, an off-the-shelf data set with labels that meets our requirements does not exist (here, we focus on predicting the quality of noisy speech and enhanced speech given by a deep-learning-based speech enhancement model). Therefore, we apply Quality-Net to predict the PESQ scores without a clean reference.

### Major Contribution

Quality-Net is the first "end-to-end", and "non-intrusive" quality

assessment model (as shown in Fig. 1) to yield "frame-level" quality.

![teaser](https://github.com/JasonSWFu/Quality-Net/blob/master/images/Quality_Net.png)

For more details and evaluation results, please check out our  [paper](https://arxiv.org/ftp/arxiv/papers/1808/1808.05344.pdf).

### Citation

If you find the code useful in your research, please cite:

    

    @inproceedings{fu2018quality,

      title={Quality-Net: An end-to-end non-intrusive speech quality assessment model based on blstm},

      author={Fu, Szu-Wei and Tsao, Yu and Hwang, Hsin-Te and Wang, Hsin-Min},

      booktitle={Interspeech},

      year={2018}}

    

### Contact

e-mail: [email protected] or [email protected]