Effects of hearing aid
signal processing on
cognitive outcome measurements
Elaine Ng
Linnaeus Centre HEAD
The Swedish Institute for Disability Research
Linköping University, Sweden
Background
• Hearing aid users are satisfied in quiet listening
environments, but not in noise.
• Different signal processing algorithms of hearing aids
are designed to improve speech perception in noise
Background
• It has been demonstrated that the benefit of signal
processing is not limited to improvement in speech
perception.
• These algorithms may also reduce listening effort,
especially in demanding listening situations
• This frees up more cognitive resources, and hence
more cognitive capacity is left available
Background
• Sarampalis et al. (2009, JSLHR)
– Noise reduction algorithm (Ephraim-Mallah)
– Normal hearing people
– Results:
• Improved performance in a word-memory task
• It makes listening less effortful, especially in adverse
listening conditions
• However, such benefit for hearing-impaired listeners
has not been reported.
Aim of the study
• This study examines how signal processing for
hearing aids (noise reduction) affects memory in
people with a hearing impairment.
Signal processing algorithm
• Binary time-frequency masking (Wang et al., 2009),
which is a noise-reducing signal processing
technique, was employed.
• Two versions of binary masking:
• Ideal binary masking (IBM)
• Realistic binary masking (NR)
• Without noise reduction
Methods
Test administration
• Dual task – an assessment of cognitive demands
1) Perceptual task
Repeat the final word immediately after
listening to each sentence
2) Free recall memory task
Report back, as many as possible, the final words
that have been repeated in the perceptual task
Example:
Pappa ska laga min fåtölj
Tanten handlar en gång i veckan
Rektorn tog fram kastrullen
Farmor åker till golfbanan
Golvet täcktes av en vit matta
Frukten packades i sex lådor
Plånboken låg kvar på isen
Farfar ska vaxa bilen
Test administration
•
A subset of the Swedish HINT sentences
(140 sentences); each sentence was presented twice
•
35 lists of 8 sentences (total 280 sentences)
•
Position of the final words in each list is analyzed
% of words correctly recalled
(primacy = position 1-3, asymptote = 4-6, recency = 7-8)
100
80
60
40
20
Primacy
0
1
2
Recency
Asymptote
3
4
5
serial position
6
7
8
Test administration
• 7 conditions; 5 repetitions per condition
NOISE REDUCTION
No processing (NoP)
Ideal BM (IBM)
Fixed at 65 dB A
Quiet
NOISE
TYPE
Realistic BM (NR)
Unmodulated speech
spectrum noise (SSN)
4-talker babble (4T)
Same individualized SNR
(95% speech recognition)
across noise conditions
• Linear amplification with individually prescribed
frequency response in all conditions
• Presentation levels were individualized to optimize
equality in listening effort across participants
Test set-up
Test administration
• Cognitive tests
– Reading span
– Word span
– Non-word span
– Lexical
– Semantic
– Physical matching
– Rhyme
Test administration
• Cognitive tests
– Reading span
– Word span
– Non-word span
– Lexical
– Semantic
– Physical matching
– Rhyme
Participants
• 26 hearing-impaired recruited
• Age: 32 – 65yr (mean = 59, SD = 7)
• Symmetrical sensorineural hearing loss:
41 – 67 dB HL (mean = 50, SD = 6.4)
• Experienced hearing aid users
• No significant history of otological disease,
chronic ear infection and sudden hearing loss
Results
v
Results
% of words correctly recalled
% of words correctly recalled
• Results of the memory performance
(All subjects, n=26)
SSN
4T
80
70
60
50
40
30
20
80
70
60
50
40
30
20
SSN/NoP
SSN/NR
SSN/IBM
Quiet
4T/NoP
4T/NR
4T/IBM
Quiet
Results
• ANOVAs show significant:
– Main effects of noise type (SSN vs 4T),
noise reduction (NoP, NR, IBM)
– Noise type x noise reduction interaction
% of words correctly recalled
100
*
80
NoP
NR
IBM
60
40
20
0
SSN
4T
Background noise
Results
– Noise type x noise reduction x serial position
• Relative improvement in the 4T background with noise reduction,
particularly for the primacy and recency items.
% of words correctly recalled
100
*
*
80
60
NoP
NR
40
IBM
20
0
primacy asymptote recency
primacy asymptote recency
SSN
4T
Results
Memory performance in most of the conditions
correlates with the reading span (RS) scores.
v
Results
• Results of the memory performance
4T
% of words correctly recalled
% of words correctly recalled
SSN
80
70
60
50
40
30
80
70
60
50
40
30
20
20
SSN/NoP
SSN/NR
SSN/IBM
All subjects (n=26)
Low reading span (n=13)
High reading span (n=13)
Quiet
4T/NoP
4T/NR
4T/IBM
All subjects (n=26)
Low reading span (n=13)
High reading span (n=13)
Quiet
Results
100
80
Quiet
60
SSN/NoP
40
SSN/NR
SSN/IBM
20
% of words correctly recalled
% of words correctly recalled
Mean memory performance as a function of position
Low reading span group (n=13)
100
80
Quiet
60
4T/NoP
40
4T/NR
4T/IBM
20
0
0
Primacy
Asymptote
Primacy
Recency
Asymptote
Recency
High reading span group (n=13)
100
80
Quiet
60
SSN/NoP
40
SSN/NR
SSN/IBM
20
% of words correctly recalled
% of words correctly recalled
100
80
Quiet
60
4T/NoP
40
4T/NR
4T/IBM
20
0
0
Primacy
Asymptote
Recency
Primacy
Asymptote
Recency
Results
• ANOVAs show significant:
– Main effect of reading span (RS) scores
– Noise reduction x RS interaction
% of words correctly recalled
100
*
80
NoP
NR
IBM
60
40
20
0
Low RS
High RS
Results
• Noise type x RS interaction
% of words correctly recalled
100
*
80
SSN
60
4T
40
20
0
Low RS
High RS
Results
• In no noise reduction (Quiet, SSN/NoP, 4T/NoP)
conditions, background noise also interacts with
reading span.
% of words correctly recalled
100
*
80
Quiet
60
SSN/NoP
40
4T/NoP
20
0
Low RS
High RS
Conclusions
Conclusions
• Binary masking noise reduction technique helped
freeing up cognitive resources.
• Memory task performance in the 4-talker
background is enhanced.
• Such enhancement occurred in both long-term
storage (primacy) and short-term storage
(recency).
Conclusions
• In individuals with better working memory
capacity,
1. there is an improvement in memory
performance with the use of binary masking,
and
2. memory performance is more disturbed in the
competing background speech than steadystate noise.
• It may serve as a tool for outcome evaluation of
different signal processing algorithms.
• T
Thank you!