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Towards a Cohort-Selective Frequency-Compression Hearing Aid

2. Sensorineural Hearing loss. Most common type of hearing lossAffects > 20 million in the US aloneCaused by physiological problems in the cochlea. 3. Traditional Hearing Aids. Amplification of frequency bandsAmplitude compressionWorks best in situations with high SNR. 4. Problems With Tr

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Towards a Cohort-Selective Frequency-Compression Hearing Aid

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    1. Towards a Cohort-Selective Frequency-Compression Hearing Aid Marie Roch¤, Richard R. Hurtig¥, Jing Lui¤, and Tong Huang¤

    2. 2 Sensorineural Hearing loss Most common type of hearing loss Affects > 20 million in the US alone Caused by physiological problems in the cochlea

    3. 3 Traditional Hearing Aids Amplification of frequency bands Amplitude compression Works best in situations with high SNR

    4. 4 Problems With Traditional Methods Simple amplification insufficient Individuals with severe hearing loss cannot perceive formants

    5. 5 Preserving the formants Frequency domain compression [Turner & Hurtig 1999] permits preservation of formants

    6. 6 Effectiveness Clinical study of 15 hearing-impaired listeners showed improvement when listening to different groups female talkers: 45% improvement male talkers: 20% improvement

    7. 7 Challenges Not all voices require the same level of compression Single setting leads to inappropriate levels of compression

    8. 8 Adaptive thresholds Decision-based control mechanism Establish cohorts and compress according to cohort class. Some possible cohorts: Phonological units Pitch Speaker “gender”

    9. 9 Gender-based classifier Selected “gender” for first study. Female, Male, Child Classifier output more stable than with phonological approaches. Broad support in the literature for the ability of both humans and machines to do this.

    10. 10 Classifier Gaussian mixture models Features extracted from 25 ms windows shifted every 10 ms Energy 12 Mel-filtered cepstral coefficients (MFCC) Time-derivatives of Energy & MFCC

    11. 11 Control system architecture

    12. 12 LDC SPIDRE Corpus Conversational telephone speech Band-limited 8 kHz Mu-law encoded Endpointed with the NIST/Kubala endpointer Train Single sides of same-gender phone calls 25 male & female Test 87 annotated cross-gender phone calls About 7 hours of calls (~5 min. each)

    13. 13 SPIDRE Classification Results

    14. 14 Error analysis Many errors occurred in fricatives which have high frequency energy

    15. 15 Evalution on TIMIT 630 speakers, clean speech 16 kHz corpus Train: 25 male, 25 female. Test 413 male, 167 female.

    16. 16 Median Smoothing (SPIDRE)

    17. 17 Conclusions & Future Work Classifier-based control systems feasible can be applied to other signal enhancement algorithms need not be limited to the cohorts presented today (e.g. auditory scene analysis)

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