New Technique for Improving Speech Intelligibility for the Hearing Impaired

1. New Technique for Improving Speech Intelligibility for the Hearing Impaired Miriam Furst-Yust School of Electrical Engineering Tel Aviv University

2. The Hearing Aid Problem • Current hearing aids are very helpful to severe and profound hearing impairment in quiet environment. • Most people with mild-to-moderate hearing loss are unable to understand speech in a noisy background. • Current Hearing aids are useless in a noisy background.

3. THE PROBLEM:common noise suppression techniques are not robust and most of the time are not effective

4. Our Approach • A single microphone followed by a cochlear model algorithm that improves the signal-to-noise ratio. • The algorithm is robust, so speech with high SNRs are not distorted.

5. Cochlear Representation Algorithm A Device that Mimics the Human Hearing and Speech Perception

6. Cochlear Representation Algorithm (CRA) • Mimic the cochlear representation of speech signals • Identify the speech areas in the cochlear representation and discard the noise areas • Reconstruct the speech signal from the modified cochlear representation

7. Cochlear Representationsof Tones Cochlear representation

8. Representation of a word Input Signal: The word “SHEN” INPUT OUTPUT Normal Ear Damaged Ear

9. Input Speech Spectrogram Time representation

10. Energy Mask Energy Estimation Energy Estimation

11. Cochlear Representation TIME (sec)

12. Modified Cochlear Representation

13. Speech Reconstruction Time representation Spectrogram

14. CRA Performance Noisy Sentence Clean Sentence CRA

15. Analysis of Human Performances with CRA:Recognition of CVC words in an open set Subjects: • Hearing Impaired with their Cochlear Implants • Hearing Impaired with their Hearing Aids • Normal Hearing

16. Word database - HAB • Hebrew adaptation (Kishon-Rabin,2002) to AB (Arthur Boothroyd) list (Boothroyd,1968) comprising CVC words. • Equal distributions in each list of phonemes in the Hebrew language • HAB common use in hearing tests. Reduces effect of frequency and/or familiarity on test scores • Two speakers: male and female. 15 lists of 10 words (total of 300 words) • Recorded at a sampling rate of 44.1 kHz

17. Applied Word database • Test subject’s ability to recognize words in noisy environments • Gaussian white noise in SNRs of 0 – 30 dB • Bandpass filter 500-8000 Hz • Apply cochlear model and reconstruction algorithm

18. Normal Hearing Performancesin open-set words identification

19. Hearing Aid Users Performancesin open-set words identification Percentage Correct (%) Signal-to-Noise Ratio (dB) No. of Subjects=51 *Hearing Impaired performance were significantly improved by CRA in SNR of 18 dB

20. Cochlear Implant Users Performancesin open-set words identification Percentage Correct (%) Signal-to-Noise Ratio (dB) No. of Subjects=16 *Hearing Impaired performance were significantly improved by CRA in SNR of 18, 24 and 30 dB

21. Why CRA is Beneficial to the Hearing Impaired but Not Effective to Normal Hearing Subjects?

22. Center Frequency = 600 Hz Center Frequency = 2500 Hz Normal Hearing Recognition rate of 2.5 Oct. Band Limited Speech

23. Summary & Conclusions: Normal Hearing People • Speech is redundant in the frequency domain. • Normal Hearing subjects can efficiently recognize speech in noisy environments because they identify the speech in different frequency bands. • CRA reduces the speech redundancy. • Therefore, CRA that is applied to a wide-band signal is not effective to normal hearing people.

24. Summary & Conclusions: Hearing Impaired People • Hearing Impaired people have band limited hearing. • The speech redundancy is not very useful to the hearing impaired. • Therefore, CRA can be very effective to the hearing impaired.

25. Hardware Solution :Real time Implementation of the Algorithm • Implementation of the algorithm as part of common digital hearing aid or Cochlear Implant.

26. Real Time Implementation