Visual Recalibration of Auditory Speech Identification

2003 ◽  
Vol 14 (6) ◽  
pp. 592-597 ◽  
Author(s):  
Paul Bertelson ◽  
Jean Vroomen ◽  
Béatrice de Gelder
Keyword(s):  
Auditory Speech ◽  
Speech Identification

Visual speech recalibrates auditory speech identification

2002 ◽  
Vol 112 (5) ◽  
pp. 2245-2245
Author(s):  
Paul Bertelson ◽  
Jean Vroomen ◽  
Beatrice de Gelder
Keyword(s):  
Visual Speech ◽  
Auditory Speech ◽  
Speech Identification

scholarly journals Speech Identification with Temporal and Spectral Modification in Subjects with Auditory Neuropathy

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Vijaya Kumar Name ◽  
C. S. Vanaja
Keyword(s):  
Signal Processing ◽  
Speech Processing ◽  
Word Identification ◽  
Low Frequency ◽  
Auditory Neuropathy ◽  
Study Word ◽  
Spectral Modification ◽  
The Individual ◽  
High Pass ◽  
Speech Identification

Background. The aim of this study was to investigate the individual effects of envelope enhancement and high-pass filtering (500 Hz) on word identification scores in quiet for individuals with Auditory Neuropathy. Method. Twelve individuals with Auditory Neuropathy (six males and six females) with ages ranging from 12 to 40 years participated in the study. Word identification was assessed using bi-syllabic words in each of three speech processing conditions: unprocessed, envelope-enhanced, and high-pass filtered. All signal processing was carried out using MATLAB-7. Results. Word identification scores showed a mean improvement of 18% with envelope enhanced versus unprocessed speech. No significant improvement was observed with high-pass filtered versus unprocessed speech. Conclusion. These results suggest that the compression/expansion signal processing strategy enhances speech identification scores—at least for mild and moderately impaired individuals with AN. In contrast, simple high-pass filtering (i.e., eliminating the low-frequency content of the signal) does not improve speech perception in quiet for individuals with Auditory Neuropathy.

scholarly journals Auditory Speech Perception Development in Relation to Patient's Age with Cochlear Implant

2016 ◽  
Vol 21 (03) ◽  
pp. 206-212 ◽  
Author(s):  
Grace Ciscare ◽  
Erika Mantello ◽  
Carla Fortunato-Queiroz ◽  
Miguel Hyppolito ◽  
Ana Reis
Keyword(s):  
Speech Perception ◽  
Cochlear Implant ◽  
Cochlear Implants ◽  
Statistical Difference ◽  
Hearing Aid ◽  
Age Groups ◽  
Auditory Thresholds ◽  
Caregiver Education ◽  
Auditory Speech ◽  
Hearing Aid Use

Introduction A cochlear implant in adolescent patients with pre-lingual deafness is still a debatable issue. Objective The objective of this study is to analyze and compare the development of auditory speech perception in children with pre-lingual auditory impairment submitted to cochlear implant, in different age groups in the first year after implantation. Method This is a retrospective study, documentary research, in which we analyzed 78 reports of children with severe bilateral sensorineural hearing loss, unilateral cochlear implant users of both sexes. They were divided into three groups: G1, 22 infants aged less than 42 months; G2, 28 infants aged between 43 to 83 months; and G3, 28 older than 84 months. We collected medical record data to characterize the patients, auditory thresholds with cochlear implants, assessment of speech perception, and auditory skills. Results There was no statistical difference in the association of the results among groups G1, G2, and G3 with sex, caregiver education level, city of residence, and speech perception level. There was a moderate correlation between age and hearing aid use time, age and cochlear implants use time. There was a strong correlation between age and the age cochlear implants was performed, hearing aid use time and age CI was performed. Conclusion There was no statistical difference in the speech perception in relation to the patient's age when cochlear implant was performed. There were statistically significant differences for the variables of auditory deprivation time between G3 - G1 and G2 - G1 and hearing aid use time between G3 - G2 and G3 - G1.

scholarly journals Performance of Severely Hearing-Impaired Children on a Closed-Response, Auditory Speech Discrimination Test

1974 ◽  
Vol 17 (3) ◽  
pp. 531-540 ◽  
Author(s):  
Kenneth O. Jones ◽  
Gerald A. Studebaker
Keyword(s):  
Hearing Impaired ◽  
Speech Discrimination ◽  
Discrimination Test ◽  
Auditory Speech ◽  
Speech Discrimination Test ◽  
Hearing Function ◽  
Response Set ◽  
Comparison Of The Results ◽  
Impaired Children ◽  
Hearing Impaired Children

The performance of 23 hearing-impaired children on a closed-response, auditory speech discrimination test and on an open-response, auditory speech discrimination test was compared to their performance on auditory tests of sensitivity, teacher-evaluated categories, and other related subject data. A comparison of the results of closed-response, auditory speech discrimination test and the open-response, auditory speech discrimination test indicates that the closed-response set test paradigm appears more productive for use with severely hearing-impaired subjects whose level of performance is low (but not 0%) on the open-response, auditory speech discrimination test. The closed-response test scores for this group are highly positively correlated to data dependent upon hearing function, whereas the open-response scores are not. Analyses of the closed-response set test results indicate that a closed-response set test paradigm can successfully demonstrate auditory speech discrimination error patterns on a subject group basis.

scholarly journals Modern Speech Identification Model using Acoustic Neural approach

2017 ◽  
Vol 6 (03) ◽  
pp. 01-06
Author(s):  
Mr. Ashish Uplenchwar
Keyword(s):  
Speech Processing ◽  
Vital Role ◽  
Reservoir Computing ◽  
Continuous Speech Recognition ◽  
Efficient Communication ◽  
Machine Interface ◽  
Recognition Systems ◽  
Speech Identification ◽  
Identification Model ◽  
Sophisticated Technology

Modern day technology demands sophisticated technology to give input commands to computational devices. Prominent techniques has to be introduced to make human machine interface smooth and compatible especially speech signals. Establishing efficient communication between computer and machine plays a vital role in speech processing. This article uses one of current technologies in the Continuous Speech Recognition systems which is Reservoir Computing based Neural Network followed by likelihood conversion. Our aim is to build a stand-alone system which understands the terminology of languages. Throughout the development, measures will be taken to keep the memory requirement and the processing time of the software as small as possible.

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