ORIGINAL ARTICLE
PREPROCESSING STRATEGIES AND SPEECH PERCEPTION IN COCHLEAR IMPLANT USERS
Asha Yathiraj 1  
,   Ashwini Rao 1
 
More details
Hide details
1
Department of Audiology, All India Institute of Speech and Hearing, Mysore, India
CORRESPONDING AUTHOR
Asha Yathiraj   

Asha Yathiraj, Department of Audiology, All India Institute of Speech and Hearing, Mysore, India, email: asha_yathiraj@rediffmail.com
Publication date: 2013-06-30
 
J Hear Sci 2013;3(2):50–59
 
KEYWORDS
ABSTRACT
Background:
The aim of the study was to investigate whether noise reduction algorithms are beneficial for speech perception in the presence of noise in children using cochlear implants. Further, the study also aimed to determine whether any difference in speech perception existed between different pre-processing strategies such as Adaptive Dynamic Range Optimization (ADRO), Autosensitivity Control (ASC), and the two-stage adaptive beam-forming algorithm (Beam) in different signal-tonoise ratios (SNRs).

Material and Method:
Speech identification scores of the participants were tested in quiet with the ‘Everyday’ default setting activated. They were also tested using speech in noise at +5 dB and +10 dB SNR with ADRO, ASC, and Beam activated. Exactly 17 children using Nucleus cochlear implants for at least 1 year were tested.

Results:
A significant difference was found between performance in quiet (in the ‘Everyday’ default setting) and in the presence of noise (with ADRO, ASC, and Beam). No significant difference was found between the 3 pre-processing strategies at both SNRs and between the 2 SNRs for all 3 strategies.

Conclusions:
In conditions where the signal and noise emerge from in front of the listener, no influence of the pre-processing strategies was seen.

 
REFERENCES (29)
1.
Tyler RS, Moore BC. Consonant recognition by some of the better cochlear-implant patients. J Acoust Soc Am, 1992; 92: 3068–77.
 
2.
Skinner MW, Clarke GM, Whitford LA et al. Evaluation of a new spectral peak coding strategy for the Nucleus 22 channel cochlear implant system. Am J Otolaryngol, 1994; 15: 15–27.
 
3.
Skinner MW, Holden LK, Holden TA et al. Speech recognition as simulated soft, conversational, and raised-to-loud vocal efforts by adults with cochlear implants. J Acoust Soc Am, 1997; 101: 3766–82.
 
4.
Rubinstein JT, Parkinson WS, Lowder MW, Gantz BJ, Nadol JB, Tyler RS. Single-channel to multichannel conversions in adult cochlear implant subjects. Am J Otolaryngol, 1998; 19: 461–66.
 
5.
Fetterman BL, Domico EH. Speech recognition in back-ground noise of cochlear implant patients. J Otolaryngol Head Neck Surg, 2002; 126: 257–63.
 
6.
Firszt JB, Holden LK, Skinner MW et al. Recognition of speech presented at soft to loud levels by adult cochlear implant recipients of three cochlear implant systems. Ear Hear, 2004; 25: 375–87.
 
7.
Spahr AJ, Dorman MF. Performance of subjects fit with the Advanced Bionics CII and Nucleus 3G cochlear implant devices. Arch Otolaryngol Head Neck Surg, 2004; 130: 624–28.
 
8.
Schafer EC, Thibodeau LM. Speech recognition abilities of adults using CIs interfaced with FM systems. J Am Acad Audiol, 2004; 15: 678–91.
 
9.
Brockmeyer AM, Potts LG. Evaluation of different signal processing options in unilateral and bilateral cochlear freedom implant recipients using R-Space background noise. J Am Acad Audiol, 2011; 22: 65–80.
 
10.
Hochberg I, Boothroyd A, Weiss M, Hellman S. Effects of noise and noise suppression on speech perception by cochlear implant users. Ear Hear, 1992; 13: 263–71.
 
11.
Skinner MW, Holden LK, Holden TA. Effect of frequency boundary assignment on speech recognition with the Speak speech-coding strategy. Annals Otol, Rhinol Laryngol Suppl, 1995; 166: 307–11.
 
12.
Kiefer J, Muller J, Pfennigdorff T et al. Speech understanding in quiet and in noise with the CIS speech coding strategy (MED-EL Combi-40) compared to the multipeak and spectral peak strategies (Nucleus). J Otorhinolaryngol Spec, 1996; 58: 127–35.
 
13.
Parkinson AJ, Parkinson WS, Tyler RS, Lowder MW, Gantz BJ. Speech perception performance in experienced cochlearimplant patients receiving the SPEAK processing strategy in the Nucleus Spectra-22 cochlear implant. J Speech Lang Hear Res, 1998; 41(5): 1073–87.
 
14.
Van Hoesel RJ, Clark GM. Evaluation of a portable two-microphone adaptive beamforming speech processor with cochlear implant patients. J Acoust Soc Am,1995; 97: 2498–503.
 
15.
James CJ, Blamey PJ, Martin L, Swanson B, Just Y, Macfarlane D. Adaptive dynamic range optimization for cochlear implants: A preliminary study. Ear Hear, 2002; 23: 49S–58S.
 
16.
Gifford RH, Revitt L J. Speech Perception for Adult Cochlear Implant Recipients in a Realistic Background Noise: Effectiveness of Preprocessing Strategies and External Options for Improving Speech Recognition in Noise. J Am Acad Audiol, 2010; 21: 441–51.
 
17.
Blamey PJ, James CJ, Wildi K, McDermott H, Martin LF. Adaptive dynamic range optimization sound processor. United States Patent Application, 1999 (6731767); 1–7.
 
18.
Dawson PW, Decker JA, Psarros CE. Optimizing Dynamic range in children using the Nucleus cochlear implant. Ear Hear, 2004; 25: 230–41.
 
19.
Patrick JF, Busby PA, Gibson PJ. The Development of the Nucleus Freedom Cochlear Implant System. Trends Amplif, 2006; 10: 175–200.
 
20.
Wolfe J, Schafer EC, Heldner B, Mulder H, Ward E, Vincent B. Evaluation of speech recognition in noise with cochlear implants and dynamic FM. J Am Acad Audiol, 2009; 20: 409–21.
 
21.
Spriet A, Van Deun L, Eftaxiadis K, Laneau J, Moonen M, van Dijk B et al. Speech under-standing in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom cochlear implant system. Ear Hear, 2007; 28: 62–72.
 
22.
Wolfe J, Schafer EC, John A, Hudson M. The effect of frontend processing on Cochlear Implant Performance of children. Otol Neurol, 2011; 32: 533–38.
 
23.
Yathiraj A, Vijayalakshmi CS. Phonemically balanced wordlist in Kannada. A test developed at the Department of Audiology, AIISH, Mysore, 2005.
 
24.
Rout A. Monosyllable speech identification test in English for Indian children. A test developed as a part of Master’s dissertation, AIISH, University of Mysore, 1996.
 
25.
Ethical Guidelines for Bio-Behavioural Research Involving Human Subjects. A publication at AIISH, Mysore; 2003.
 
26.
ANSI S3.1. American National Standards Institute. Maximum permissible ambient noise for audiometric test rooms. ANSI S3.1 (1999). New York: American National Institute.
 
27.
Dirks DD, Morgan DE, Dubno JR. A procedure for quantifying the effects of noise on Speech recognition. J Speech Hear Disord, 1982; 47: 114–23.
 
28.
Finitzo-Hieber T, Tillman TW. Room acoustics effects on monosyllabic word discrimination ability for normal and hearingimpaired children. J Speech Hear Res,1978; 21: 440–58.
 
29.
Johnson, CE. Children’s phoneme identification in reverberation and noise. J Speech, Lang, Hear Res, 2000; 43: 144–57.