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Speech Intelligibility Enhancement Algorithm Based on Multi-Resolution Power-Normalized Cepstral Coefficients (MRPNCC) for Digital Hearing Aids

Xia Wang1, Xing Deng2,3, Hongming Shen1,*, Guodong Zhang1, Shibing Zhang1
1 School of Information Science and Technology, Nantong University, Nantong, 226019, China
2 School of Automation, Key Laboratory of Measurement and Control for CSE, Ministry of Education, Southeast University, Nanjing, 210096, China
3 School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
* Corresponding Author: Hongming Shen. Email:

Computer Modeling in Engineering & Sciences 2021, 126(2), 693-710.

Received 29 July 2020; Accepted 23 November 2020; Issue published 21 January 2021


Speech intelligibility enhancement in noisy environments is still one of the major challenges for hearing impaired in everyday life. Recently, Machine-learning based approaches to speech enhancement have shown great promise for improving speech intelligibility. Two key issues of these approaches are acoustic features extracted from noisy signals and classifiers used for supervised learning. In this paper, features are focused. Multi-resolution power-normalized cepstral coefficients (MRPNCC) are proposed as a new feature to enhance the speech intelligibility for hearing impaired. The new feature is constructed by combining four cepstrum at different time–frequency (T–F) resolutions in order to capture both the local and contextual information. MRPNCC vectors and binary masking labels calculated by signals passed through gammatone filterbank are used to train support vector machine (SVM) classifier, which aim to identify the binary masking values of the T–F units in the enhancement stage. The enhanced speech is synthesized by using the estimated masking values and wiener filtered T–F unit. Objective experimental results demonstrate that the proposed feature is superior to other comparing features in terms of HIT-FA, STOI, HASPI and PESQ, and that the proposed algorithm not only improves speech intelligibility but also improves speech quality slightly. Subjective tests validate the effectiveness of the proposed algorithm for hearing impaired.


Speech intelligibility enhancement; multi-resolution power-normalized cepstral coefficients; binary masking value; hearing impaired

Cite This Article

Wang, X., Deng, X., Shen, H., Zhang, G., Zhang, S. (2021). Speech Intelligibility Enhancement Algorithm Based on Multi-Resolution Power-Normalized Cepstral Coefficients (MRPNCC) for Digital Hearing Aids. CMES-Computer Modeling in Engineering & Sciences, 126(2), 693–710.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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