2009Journal of Biomedical Engineering ResearchRequires access

A Study on Acoustic Masking Effect by Frame-Based Formant Enhancement

Yu-Yong Jeon, Kyu‐Sung Kim, Sangmin Lee

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Abstract

One of the characteristics of the hearing impaired is that their frequency selectivity is poorer than that of the normal hearing. To compensate this, formant enhancement algorithms and spectral contrast enhancement algorithms have been developed. However in some cases, these algorithms fail to improve the frequency selectivity of the hearing impaired. One of the reasons is the acoustic masking among enhanced formants. In this study, we tried to enhance the formants based on the individual masking characteristic of each subject. The masking characteristic used in this study was minimum level difference (MLD) between the first formant to the second formant while acoustic masking was occurred. If the level difference between the two formants in each frame is larger than the MLD, the gain of the first formant was decreased to reduce the acoustic masking that occurred among formants. As a result of the speech discrimination test, using formant enhanced speeches, speech discrimination score (SDS) of the speeches having differently enhanced formants was significantly superior to SDS of the speeches having equally enhanced formants. It means that suppression of the acoustic masking among formants improve frequency selectivity of the hearing impaired.

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What this paper is about

One of the characteristics of the hearing impaired is that their frequency selectivity is poorer than that of the normal hearing. To compensate this, formant enhancement algorithms and spectral contrast enhancement algorithms have been developed. However in some cases, these algorithms fail to improve the frequency selectivity of the hearing impaired. One of the reasons is the acoustic masking among enhanced formants. In this study, we tried to enhance the formants based on the individual masking characteristic of each subject. The masking characteristic used in this study was minimum level difference (MLD) between the first formant to the second formant while acoustic masking was occurred. If the level difference between the two formants in each frame is larger than the MLD, the gain of the first formant was decreased to reduce the acoustic masking that occurred among formants. As a result of the speech discrimination test, using formant enhanced speeches, speech discrimination score (SDS) of the speeches having differently enhanced formants was significantly superior to SDS of the speeches having equally enhanced formants. It means that suppression of the acoustic masking among formants improve frequency selectivity of the hearing impaired.

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Available abstract

One of the characteristics of the hearing impaired is that their frequency selectivity is poorer than that of the normal hearing. To compensate this, formant enhancement algorithms and spectral contrast enhancement algorithms have been developed. However in some cases, these algorithms fail to improve the frequency selectivity of the hearing impaired. One of the reasons is the acoustic masking among enhanced formants. In this study, we tried to enhance the formants based on the individual masking characteristic of each subject. The masking characteristic used in this study was minimum level difference (MLD) between the first formant to the second formant while acoustic masking was occurred. If the level difference between the two formants in each frame is larger than the MLD, the gain of the first formant was decreased to reduce the acoustic masking that occurred among formants. As a result of the speech discrimination test, using formant enhanced speeches, speech discrimination score (SDS) of the speeches having differently enhanced formants was significantly superior to SDS of the speeches having equally enhanced formants. It means that suppression of the acoustic masking among formants improve frequency selectivity of the hearing impaired.

Key concepts: Formant, Masking (illustration), Hearing impaired, Acoustics, Speech recognition, Audiology, Computer science, Physics

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