Xia Wang¹, Xing Deng^2,3, Hongming Shen^1,*, Guodong Zhang¹, Shibing Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 693-710, 2021, DOI:10.32604/cmes.2021.013186

Abstract 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… More >

T. Asakura^*, K. Shindo

Sound & Vibration, Vol.54, No.2, pp. 85-98, 2020, DOI:10.32604/sv.2020.08119

Abstract A number of studies have been conducted on the improvement of the sound quality of electrical artificial laryngeal speech, the speech produced has been difficult to hear compared to a natural voice. For this reason, it is necessary to effectively improve the frequency characteristics of the input signal. In the present study, to improve the sound quality of vocalization using an electrical artifi- cial larynx, first, a comparison of the frequency characteristics between the real and artificial voices was conducted, and three filters that can make the frequency characteristics of the artificial voice closer to those of a real voice… More >