To facilitate the process of developing speech perception, speech-language pathologists have to teach a subject with hearing loss the differences between two syllables by manually enhancing acoustic cues of speech. However, this process is time consuming and difficult. Thus, this study proposes an objective approach to automatically identify the regions of spectral distinctiveness between two syllables, which is used for speech-perception training. To accurately represent the characteristics of speech, mel-frequency cepstrum coefficients are selected as analytical parameters. The mismatch between two syllables in time domain is handled by dynamic time warping. Further, a filter bank is adopted to estimate the components in different frequency bands, which are also represented as mel-frequency cepstrum coefficients. The spectral distinctiveness in different frequency bands is then easily estimated by using Euclidean metrics. Finally, a morphological gradient operator is applied to automatically identify the regions of spectral distinctiveness. To evaluate the proposed approach, the identified regions are manipulated and then the manipulated syllables are measured by a close-set based speech-perception test. The experimental results demonstrated that the identified regions of spectral distinctiveness are very useful in speech perception, which indeed can help speech-language pathologists in speech-perception training.
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