Download Cochlear Representation Algorithm (CRA)

New Technique for Improving Speech
Intelligibility for the Hearing Impaired
Miriam Furst-Yust
School of Electrical Engineering
Tel Aviv University
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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.
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THE PROBLEM:
common noise suppression
techniques are not robust and
most of the time are not effective
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Our Approach

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A single microphone followed by a cochlear
model algorithm that improves the signal-tonoise ratio.
The algorithm is robust, so speech with high
SNRs are not distorted.
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Cochlear Representation Algorithm
A Device that Mimics
the Human Hearing
and Speech Perception
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Cochlear Representation Algorithm (CRA)
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
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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
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Cochlear Representations of
Tones
Cochlear representation
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Representation of a word
Input Signal: The word “SHEN”
INPUT
OUTPUT
Normal
Ear
Damaged
Ear
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Input Speech
Time representation
Spectrogram
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Energy Estimation
Energy Estimation
Energy Mask
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Cochlear
Representation
TIME (sec)
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Modified Cochlear Representation
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Speech Reconstruction
Time representation
Spectrogram
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CRA Performance
Noisy Sentence
MELP
2.4 Kb/s
Clean Sentence
G729
8 Kb/s
MELP
2.4 Kb/s
G729
8 Kb/s
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Analysis of Human
Performances with CRA:
Recognition of CVC words in an open set
Subjects:
1. Hearing Impaired with their Cochlear
Implants
2. Hearing Impaired with their Hearing Aids
3. Normal Hearing
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Word database - HAB



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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
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Applied Word database

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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
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Normal Hearing Performances
in open-set words identification
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Hearing Aid Users Performances
in open-set words identification
Percentage
Correct (%)
70
60
50
Hearing Aid
40
Hearing
Aid+CRA
30
20
10
0
No Noise
30
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Signal-to-Noise Ratio (dB)
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No. of Subjects=51
* Hearing Impaired performance were significantly
improved by CRA in SNR of 18 dB
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Cochlear Implant Users
Performances
in open-set words identification
Percentage
Correct (%)
60
50
Hearing Aid
40
30
Hearing
Aid+CRA
20
10
0
No Noise
30
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Signal-to-Noise Ratio (dB)
18
No. of Subjects=16
* Hearing Impaired performance were significantly
improved by CRA in SNR of 18, 24 and 30 dB
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Why CRA is Beneficial to the
Hearing Impaired
but Not Effective
to Normal Hearing Subjects?
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Normal Hearing Recognition rate of
2.5 Oct. Band Limited Speech
Center Frequency = 600 Hz
100
90
80
70
60
50
40
30
20
10
0
600 Hz
2500
Center Frequency = 2500
HzHz
No filter
Clean
SNR=0 dB
SNR=3 dB
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Summary & Conclusions:
Normal Hearing People
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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.
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Summary & Conclusions:
Hearing Impaired People


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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.
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Hardware Solution :
Real time Implementation of the Algorithm

Implementation of the algorithm as part
of common digital hearing aid or Cochlear
Implant.
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Real Time Implementation
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