Predicting the Intelligibility of Cochlear-implant Vocoded Speech from Objective Quality Measure(1) Department of Electrical Engineering, The University.

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Predicting the Intelligibility of Cochlear-implant Vocoded Speech from Objective Quality Measure(1) Department of Electrical Engineering, The University of Texas at Dallas, Richardson, Texas75083, USA Received 8 Jan 2011; Accepted 27 May 2011; doi: /jmbe.885 Chairman:Hung-Chi Yang Presenter: Yu-Kai Wang Advisor: Dr. Yeou-Jiunn Chen Date:

Outline  Introduction  Purposes  Material and Methods

Introduction (1)  Cochlear implants (CIs)  Restore partial hearing to patients with severe to profound deafness.  A number of factors may affect performance.  Electrode insertion depth and placement  Quiet and noisy conditions  Electric-acoustic stimulation (EAS)  An electrode array  It is implanted only partially into the cochlea so as to preserve the residual acoustic hearing.  (20-60 dB hearing loss (HL) up to 750 Hz and severe-to-profound hearing loss at 1000 Hz and above)

Introduction (2)  A speech intelligibility index  To predict the intelligibility of vocoded speech.  To guide development of new speech processing strategies for cochlear implants.  It is highly correlated with the perceptual evaluation of speech quality (PESQ) measure.  Originally designed for predicting subjective speech quality.

Purposes  Assess the performance of conventional objective measures  Applied to predicting the intelligibility of vocoded speech.

Material and Methods  2.1 Subjects  Speech intelligibility data were collected from three listening experiments using NH listeners as subjects.  Vocoded English  Vocoded Mandarin Chinese  Experiments 1 and 2( by Chen and Loizou )  Assessed the contribution of weak consonants to vocoded English speech intelligibility in noisy environments.  Experiment 3  Predicting the intelligibility of vocoded Mandarin sentences.

Material and Methods  Details of the subjects and test conditions for the three experiments are given in Tabel 1. steady-state noise electric-acoustic stimulation 2054

Material and Methods  2.2 Stimuli  English and Chinese sentence contained 8 and 7 words on average, respectively.  Two types of masker were used to corrupt the sentences.  Continuous steady-state noise (SSN)  Long-term spectrum was the same as those of the test sentences.  Two equal-level interfering female talkers (2-talker).

Material and Methods  The test of vocoded English, the sentences were corrupted.  -5, 0, and 5 dB SNR levels  The test of Mandarin Chinese , the sentences were corrupted.  -4, 0, 4, 8, and 12 dB SNR levels  The EAS-vocoder , the sentences were corrupted.  The SSN and 2-talker maskers  -4, -2, 0, 2, and 4 dB SNR levels  The above SNR levels  It were selected to avoid ceiling/floor effects for speech intelligibility data.

Material and Methods  2.3 Signal processing  The stimuli were presented in two signal processing conditions.  Tone-vocoder.  EAS-vocoder.  The first processing condition (tone-vocoder).  To simulate eight-channel electrical stimulation.  Eightd-channel sinewave-excited vocoder.  Through a pre -emphasis filter (2000-Hz cutoff) with 3 dB/octave roll-off.  Band-passed into eight frequency bands btween 80 and 6000 Hz.  Sixth-order Butterworth filter.

Material and Methods  The envelope of the signal was extracted by.  Full-wave rectification  Low –pass filtering using a second-order Butterworth filter (400- Hz cutoff).  Sinusoids were generated with amplitudes equal to the root- mean-square of the envelopes (computed every 4 ms).  Frequencies equal to the center frequencies of the bandpass filters.

Material and Methods  The second processing condition (EAS-vocoder).  Simulated combined electric-acoustic stimulation.  The signal was first low-pass (LP)-filtered to 600 Hz using a sixth-order Butterworth filter.  To simulate the effects of EAS for patients with residual hearing below 600 Hz.  The LP stimulus was combined with the upper five channels of the eight-channel tone-vocoder.

Material and Methods