PURPOSE: The benefits of amplitude compression in hearing aids may be limited by distortion resulting from rapid gain adjustment. To evaluate this, it is convenient to quantify distortion by using a metric that is sensitive to the changes in the processed signal that decrease consonant recognition, such as the Envelope Difference Index (EDI; Fortune, Woodruff, & Preves, 1994). However, the EDI relies on the entire duration of the signal, including portions irrelevant to consonant recognition. METHOD: This note describes a computationally efficient method of automatically segmenting speech in time according to the syllable structure. Our technique uses the 1st derivative of the envelope as a basis. Peaks located in the derivative were used to generate a weighting function for the computation of a metric of signal distortion. RESULTS: The weighting function significantly improved the variance explained in consonant recognition scores over previous methods. However, only 3.2% of the variance was explained in the revised model. CONCLUSION: This technique was effective in focusing the analysis of distortion on specific segments of the signal. Use of the technique has implications for speech analysis. The difference in the amplitude envelope of consonants is not a robust model of the effect of hearing aid compression on consonant recognition.
PURPOSE: The benefits of amplitude compression in hearing aids may be limited by distortion resulting from rapid gain adjustment. To evaluate this, it is convenient to quantify distortion by using a metric that is sensitive to the changes in the processed signal that decrease consonant recognition, such as the Envelope Difference Index (EDI; Fortune, Woodruff, & Preves, 1994). However, the EDI relies on the entire duration of the signal, including portions irrelevant to consonant recognition. METHOD: This note describes a computationally efficient method of automatically segmenting speech in time according to the syllable structure. Our technique uses the 1st derivative of the envelope as a basis. Peaks located in the derivative were used to generate a weighting function for the computation of a metric of signal distortion. RESULTS: The weighting function significantly improved the variance explained in consonant recognition scores over previous methods. However, only 3.2% of the variance was explained in the revised model. CONCLUSION: This technique was effective in focusing the analysis of distortion on specific segments of the signal. Use of the technique has implications for speech analysis. The difference in the amplitude envelope of consonants is not a robust model of the effect of hearing aid compression on consonant recognition.
Authors: Pamela E Souza; Gregory Ellis; Kendra Marks; Richard Wright; Frederick Gallun Journal: J Speech Lang Hear Res Date: 2021-05-21 Impact factor: 2.297