4aPPa32. How Susceptibility To Noise Varies Across Speech Frequencies

Slides:



Advertisements
Similar presentations
Considerations for the Development and Fitting of Hearing-Aids for Auditory-Visual Communication Ken W. Grant and Brian E. Walden Walter Reed Army Medical.
Advertisements

Frequency Band-Importance Functions for Auditory and Auditory- Visual Speech Recognition Ken W. Grant Walter Reed Army Medical Center Washington, D.C.
Introduction to MP3 and psychoacoustics Material from website by Mark S. Drew
Acousteen Herman J.M. Steeneken Subjective Intelligibility Assessment Dr. Herman J.M. Steeneken.
Hearing relative phases for two harmonic components D. Timothy Ives 1, H. Martin Reimann 2, Ralph van Dinther 1 and Roy D. Patterson 1 1. Introduction.
Effects of Competence, Exposure, and Linguistic Backgrounds on Accurate Production of English Pure Vowels by Native Japanese and Mandarin Speakers Malcolm.
The perception of dialect Julia Fischer-Weppler HS Speaker Characteristics Venice International University
3pSC9. Effect of reduced audibility on masking release for normal- and hard-of-hearing listeners Peggy Nelson, Yingjiu Nie, Elizabeth Anderson, Bhagyashree.
Advanced Speech Enhancement in Noisy Environments
Introduction Relative weights can be estimated by fitting a linear model using responses from individual trials: where g is the linking function. Relative.
Speech perception 2 Perceptual organization of speech.
Acknowledgments This study was funded in part by a grant from the National Institutes of Health-Institute on Deafness and other Communicative Disorders.
The nature of sound Types of losses Possible causes of hearing loss Educational implications Preparing students for hearing assessment.
Speaking Style Conversion Dr. Elizabeth Godoy Speech Processing Guest Lecture December 11, 2012.
Vocal Emotion Recognition with Cochlear Implants Xin Luo, Qian-Jie Fu, John J. Galvin III Presentation By Archie Archibong.
Interrupted speech perception Su-Hyun Jin, Ph.D. University of Texas & Peggy B. Nelson, Ph.D. University of Minnesota.
1 New Technique for Improving Speech Intelligibility for the Hearing Impaired Miriam Furst-Yust School of Electrical Engineering Tel Aviv University.
Cross-Spectral Channel Gap Detection in the Aging CBA Mouse Jason T. Moore, Paul D. Allen, James R. Ison Department of Brain & Cognitive Sciences, University.
1 Recent development in hearing aid technology Lena L N Wong Division of Speech & Hearing Sciences University of Hong Kong.
Preemphasis and Deemphasis Filtering
Interarticulator programming in VCV sequences: Effects of closure duration on lip and tongue coordination Anders Löfqvist Haskins Laboratories New Haven,
Adaptive Design of Speech Sound Systems Randy Diehl In collaboration with Bjőrn Lindblom, Carl Creeger, Lori Holt, and Andrew Lotto.
Speech Enhancement Using Spectral Subtraction
From Auditory Masking to Supervised Separation: A Tale of Improving Intelligibility of Noisy Speech for Hearing- impaired Listeners DeLiang Wang Perception.
METHODOLOGY INTRODUCTION ACKNOWLEDGEMENTS LITERATURE Low frequency information via a hearing aid has been shown to increase speech intelligibility in noise.
Speech Perception 4/4/00.
1 Loudness and Pitch Be sure to complete the loudness and pitch interactive tutorial at … chophysics/pitch/loudnesspitch.html.
Sh s Children with CIs produce ‘s’ with a lower spectral peak than their peers with NH, but both groups of children produce ‘sh’ similarly [1]. This effect.
Sounds in a reverberant room can interfere with the direct sound source. The normal hearing (NH) auditory system has a mechanism by which the echoes, or.
Calibration of Consonant Perception in Room Reverberation K. Ueno (Institute of Industrial Science, Univ. of Tokyo) N. Kopčo and B. G. Shinn-Cunningham.
Hearing Research Center
1 Cross-language evidence for three factors in speech perception Sandra Anacleto uOttawa.
P. N. Kulkarni, P. C. Pandey, and D. S. Jangamashetti / DSP 2009, Santorini, 5-7 July DSP 2009 (Santorini, Greece. 5-7 July 2009), Session: S4P,
Functional Listening Evaluations:
Staffan Hygge Noise, memory and learning (Buller, minne och inlärning) Staffan Hygge Environmental Psychology Department of Building, Energy and Environmental.
Katherine Morrow, Sarah Williams, and Chang Liu Department of Communication Sciences and Disorders The University of Texas at Austin, Austin, TX
Predicting the Intelligibility of Cochlear-implant Vocoded Speech from Objective Quality Measure(1) Department of Electrical Engineering, The University.
Evaluation of a Binaural FMV Beamforming Algorithm in Noise Jeffery B. Larsen, Charissa R. Lansing, Robert C. Bilger, Bruce Wheeler, Sandeep Phatak, Nandini.
A. R. Jayan, P. C. Pandey, EE Dept., IIT Bombay 1 Abstract Perception of speech under adverse listening conditions may be improved by processing it to.
Figures for Chapter 8 Candidacy Dillon (2001) Hearing Aids.
Predicting Speech Intelligibility Where we were… Model of speech intelligibility Good prediction of Greenberg’s bands Data.
Date of download: 5/27/2016 Copyright © 2016 American Medical Association. All rights reserved. From: The Importance of High-Frequency Audibility in the.
What can we expect of cochlear implants for listening to speech in noisy environments? Andrew Faulkner: UCL Speech Hearing and Phonetic Sciences.
Date of download: 6/3/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Outcomes for Cochlear Implant Users With Significant.
HOW WE TRANSMIT SOUNDS? Media and communication 김경은 김다솜 고우.
Speech Enhancement Algorithm for Digital Hearing Aids
Speech and Singing Voice Enhancement via DNN
Relationship between Pitch and Rhythm Perception with Tonal Sequences
Copyright © American Speech-Language-Hearing Association
Precedence-based speech segregation in a virtual auditory environment
Sandra J. Guzman1, Cody Elston1, Valeriy Shafiro2 & Stanley Sheft2
Bi-dialectalism: the investigation of the cognitive advantage and non-native dialect perception in noise Brittany Moore, Jackie Rayyan, & Lynn Gilbertson,
Copyright © American Speech-Language-Hearing Association
Modulation Error Ratio and Signal-to-Noise Ratio Demystified Presented by Sunrise Telecom Broadband …a step ahead.
Copyright © American Speech-Language-Hearing Association
Figure 1. Three light curves of IRAS 13224–3809 in 600-s bins
Evaluation of Classroom Audio Distribution and Personal FM Systems
Within a Mixed-Frequency Visual Environment
Temporal Processing and Adaptation in the Songbird Auditory Forebrain
11/24/2018 Sensory Re-Weighting In Human Postural Control During Moving-Scene Perturbations A. Mahboobin1, P. Loughlin1,2, Ph.D., M. Redfern3,2, Ph.D.,
Volume 77, Issue 5, Pages (March 2013)
Evolution of human vocal production
Speech Perception (acoustic cues)
8.5 Modulation of Signals basic idea and goals
Volume 49, Issue 3, Pages (February 2006)
Volume 54, Issue 6, Pages (June 2007)
Attentive Tracking of Sound Sources
Temporal Processing and Adaptation in the Songbird Auditory Forebrain
Jozsef Csicsvari, Hajime Hirase, Akira Mamiya, György Buzsáki  Neuron 
Volume 9, Pages (November 2018)
Presentation transcript:

4aPPa32. How Susceptibility To Noise Varies Across Speech Frequencies Sarah E. Yoho1, Eric W. Healy, Frédéric Apoux2 Department of Speech and Hearing Science The Ohio State University Currently at Department of Communicative Disorders and Deaf Education, Utah State University Currently at Department of Otolaryngology - Head & Neck Surgery, The Ohio State University Purpose: To determine the particular susceptibility to noise of individual bands of speech 1. Intelligibility Functions 4. Importance / Susceptibility General Method: IEEE sentences (IEEE, 1969) 21 ‘critical bands’ as specified in ANSI SII (ANSI, 1997) 10 talkers (5 male), general American dialect Subjects all young adults, normal hearing, native speakers of English Baseline Scores: Band absent and band present in quiet scores used as baseline intelligibility (from Yoho et al., in review) 20 subjects randomly assigned into three groups (Bands 1-7; 8-14; 15-21) Target band presented with four other bands randomly assigned from trial-to-trial (band present), or four randomly-assigned other bands presented without target (band absent) 20 sentences/band condition; total of 140 sentences/subject Broadband speech set to 70 dBA Noise Susceptibility: 40 subjects divided into two groups (odd bands; even bands) Band 1 excluded due to very low importance value Target band presented with 4 other bands, randomly assigned from trial-to-trial Gaussian noise added to target band at six SNRs: -12, -8, -4, 0, 4, 8 dB Noise had 10-ms raised cosine rise/fall, started at least 300 ms prior to speech 10 sentences/ condition, 60 total conditions: (10 Target Bands x 6 SNRs) Noise Susceptibility Definition: Amount of noise (SNR) required to reduce performance for that target band halfway from band present in quiet to band absent in quiet Fig 6. Relationship between noise susceptibility (in dB SNR) and band importance for the twenty bands tested here (r = -0.238, p = .312). Fig 2. Sentence intelligibility in percent correct as a function of target band signal-to-noise ratio for the ten even-numbered critical bands. The dashed top and bottom lines in each panel are average ‘band-present’ and ‘band absent’ sentence intelligibility, respectively, for that target band. The dotted line in each panel is the midway point between band present and band absent for that target band. Fig 3. Same as for Fig 2., but for the ten odd-numbered critical bands tested. Results and Conclusions: Results suggest that susceptibility to noise is not equal across the speech spectrum There is large variability in noise susceptibility across the spectrum, with the lowest band being very susceptible, a region of low susceptibility in low frequencies, and no consistent pattern in high frequencies These findings are despite the use of multiple talkers- therefore they do not simply reflect any particular aspect of an individual voice There is no systematic relationship between noise susceptibility and band importance Some bands of high importance (bands with center frequencies of 1370 and 1850 Hz) displayed high susceptibility to noise Possible implications for evaluating band importance in the presence of background noise 3. Noise Susceptibility Values (SNR) 2. Additional Functions Fig 4. Same as for Figs. 2 and 3, but data from a new group of five subjects for bands 2 and 20. The dashed ‘band-present’ and ‘band-absent’ scores were obtained from these same five subjects. Fig 5. Noise susceptibility as equivalent signal-to-noise ratios for the target bands indicated. Values for bands having center frequencies of 250 and 7000 Hz are from the group of five subjects shown in Fig 4. Work supported in part by NIDCD grants R01 DC008594 and R01 DC 015521 to EWH American National Standard Inst. (1997). ANSI S3.5 (R2007). American National Standard Methods for the Calculation of the Speech Intelligibility Index (American National Standards Inst., New York). Apoux, F., and Healy, E. W. (2012). “Use of a compound approach to derive auditory-filter-wide frequency-importance functions for vowels and consonants,” J. Acoust. Soc. Am. 132, 1078-1087. Healy, E. W., Yoho, S. E., & Apoux, F. (2013). “Band importance for sentences and words reexamined,” J. Acoust. Soc. Am., 133, 463-473. IEEE (1969). “IEEE recommended practice for speech quality measurements,” IEEE Trans. Audio Electroacoust. 17, 225–246. Fig 1. From Yoho et al. (in review). Band importance functions for IEEE sentences by a single male talker and by 5 male and 5 female talkers. The multi-talker function (open symbols) was used for comparison here.