The Effects of Noise on Speech Recognition in Cochlear Implant Subjects: Predictions and Analysis Using Acoustic Models

Cochlear implants can provide partial restoration of hearing, even with limited spectral resolution and loss of fine temporal structure, to severely deafened individuals. Studies have indicated that background noise has significant deleterious effects on the speech recognition performance of cochlear implant patients. This study investigates the effects of noise on speech recognition using acoustic models of two cochlear implant speech processors and several predictive signal-processing-based analyses. The results of a listening test for vowel and consonant recognition in noise are presented and analyzed using the rate of phonemic feature transmission for each acoustic model. Three methods for predicting patterns of consonant and vowel confusion that are based on signal processing techniques calculating a quantitative difference between speech tokens are developed and tested using the listening test results. Results of the listening test and confusion predictions are discussed in terms of comparisons between acoustic models and confusion prediction performance.

[1]  M. Dorman,et al.  Speech intelligibility as a function of the number of channels of stimulation for signal processors using sine-wave and noise-band outputs. , 1997, The Journal of the Acoustical Society of America.

[2]  Q J Fu,et al.  Recognition of time-distorted sentences by normal-hearing and cochlear-implant listeners. , 2001, The Journal of the Acoustical Society of America.

[3]  P J Blamey,et al.  Speech processing studies using an acoustic model of a multiple-channel cochlear implant. , 1984, The Journal of the Acoustical Society of America.

[4]  M F Dorman,et al.  Recognition of Sentences in Noise by Normal-Hearing Listeners Using Simulations of Speak-Type Cochlear Implant Signal Processors , 2000, The Annals of otology, rhinology & laryngology. Supplement.

[5]  Philipos C Loizou,et al.  The intelligibility of speech with "holes" in the spectrum. , 2002, The Journal of the Acoustical Society of America.

[6]  Stuart Rosen,et al.  Simulations of tonotopically mapped speech processors for cochlear implant electrodes varying in insertion depth. , 2003, The Journal of the Acoustical Society of America.

[7]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[8]  Edwin C. Moxon,et al.  Physiological Considerations in Artificial Stimulation of the Inner Ear , 1972, The Annals of otology, rhinology, and laryngology.

[9]  H Müsch,et al.  Using statistical decision theory to predict speech intelligibility. I. Model structure. , 2001, The Journal of the Acoustical Society of America.

[10]  P C Loizou,et al.  Speech recognition by normal-hearing and cochlear implant listeners as a function of intensity resolution. , 2000, The Journal of the Acoustical Society of America.

[11]  G. A. Miller,et al.  An Analysis of Perceptual Confusions Among Some English Consonants , 1955 .

[12]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[13]  M F Dorman,et al.  The recognition of sentences in noise by normal-hearing listeners using simulations of cochlear-implant signal processors with 6-20 channels. , 1998, The Journal of the Acoustical Society of America.

[14]  A van Wieringen,et al.  Natural vowel and consonant recognition by Laura cochlear implantees. , 1999, Ear and hearing.

[15]  G. E. Peterson,et al.  Control Methods Used in a Study of the Vowels , 1951 .

[16]  Peggy B Nelson,et al.  Understanding speech in modulated interference: cochlear implant users and normal-hearing listeners. , 2003, The Journal of the Acoustical Society of America.

[17]  Philipos C. Loizou,et al.  Word Recognition by Children Listening to Speech Processed into a Small Number of Channels: Data from Normal-Hearing Children and Children with Cochlear Implants , 2000, Ear and Hearing.

[18]  M A Svirsky,et al.  Mathematical modeling of vowel perception by users of analog multichannel cochlear implants: temporal and channel-amplitude cues. , 2000, The Journal of the Acoustical Society of America.

[19]  D. D. Greenwood.  A cochlear frequency-position function for several species--29 years later. , 1990, The Journal of the Acoustical Society of America.

[20]  R. Shannon,et al.  Speech recognition in noise as a function of the number of spectral channels: comparison of acoustic hearing and cochlear implants. , 2001, The Journal of the Acoustical Society of America.

[21]  T. M. Nearey,et al.  Identification of resynthesized /hVd/ utterances: effects of formant contour. , 1999, The Journal of the Acoustical Society of America.

[22]  S. Howard Bartley,et al.  The relation of pitch to frequency. , 1950 .

[23]  A. Leijon,et al.  Estimation of sensory information transmission using a hidden Markov model of speech stimuli , 2001 .

[24]  E. Domico,et al.  Speech Recognition in Background Noise of Cochlear Implant Patients , 2002, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[25]  G M Clark,et al.  Comparison of Two Speech Processing Schemes Using Normal-hearing Subjects. , 1990 .

[26]  Eric R. Ziegel,et al.  Probability and Statistics for Engineering and the Sciences , 2004, Technometrics.

[27]  Leslie M Collins,et al.  The effect of channel interactions on speech recognition in cochlear implant subjects: predictions from an acoustic model. , 2002, The Journal of the Acoustical Society of America.

[28]  H Müsch,et al.  Using statistical decision theory to predict speech intelligibility. II. Measurement and prediction of consonant-discrimination performance. , 2001, The Journal of the Acoustical Society of America.

[29]  M F Dorman,et al.  The Identification of Speech in Noise by Cochlear Implant Patients and Normal‐Hearing Listeners Using 6‐Channel Signal Processors , 1998, Ear and hearing.

[30]  Michael F Dorman,et al.  A comparison of the speech understanding provided by acoustic models of fixed-channel and channel-picking signal processors for cochlear implants. , 2002, Journal of speech, language, and hearing research : JSLHR.

[31]  R. Shannon,et al.  Effect of stimulation rate on phoneme recognition by nucleus-22 cochlear implant listeners. , 2000, The Journal of the Acoustical Society of America.

[32]  A. Thornton,et al.  Speech-discrimination scores modeled as a binomial variable. , 1978, Journal of speech and hearing research.

[33]  A Parkinson,et al.  Channel weights for speech recognition in cochlear implant users. , 2001, The Journal of the Acoustical Society of America.

[34]  A. Faulkner,et al.  Adaptation by normal listeners to upward spectral shifts of speech: implications for cochlear implants. , 1999, The Journal of the Acoustical Society of America.

[35]  S. Soli,et al.  Development of the Hearing in Noise Test for the measurement of speech reception thresholds in quiet and in noise. , 1994, The Journal of the Acoustical Society of America.

[36]  P C Loizou,et al.  Minimum spectral contrast needed for vowel identification by normal hearing and cochlear implant listeners. , 2001, The Journal of the Acoustical Society of America.

[37]  Q J Fu,et al.  Effects of noise and spectral resolution on vowel and consonant recognition: acoustic and electric hearing. , 1998, The Journal of the Acoustical Society of America.

[38]  Deniz Baskent,et al.  Speech recognition under conditions of frequency-place compression and expansion. , 2003, The Journal of the Acoustical Society of America.

[39]  G M Clark,et al.  An acoustic model of a multiple-channel cochlear implant. , 1984, The Journal of the Acoustical Society of America.