Speech Based Optimization of Hearing Devices
Alice E. Holmes, Rahul Shrivastav, Hannah W. Siburt & Lee Krause
The Problem
Programming is based on electrically measured dynamic ranges of pulsed stimuli (non-speech)
Current programming methods have numerous options
Purpose
The goal is to understand speech, the tuning of the device should be based on speech and not tones.
Development of a standard metric to understand the strengths and weaknesses of the individual device user.
The complexity of problem requires an automated and intelligent process to optimize the device programming.
Overview
Hearing
Device, DBrain, B
Input Signal, Sinp
Output Signal, Sout
Intermediate Signal, Sint
inp int
int out
inp out
D S S
B S S
B D S S
inp outWe want :
. . . .
S S
i e B D I
Almost nothing is known about the function B
What to optimize?
Acoustic contrasts essential for speech intelligibility-- Minimize error function
From patient experiments, we can get data for different values of the parameters and the corresponding errors– The dimensionality of this data is related to the
number of independent programmable parameters– Many parameters, hence very high dimensionality
leading to the “curse of dimensionality”
How to reduce the complexity of the problem?
Artificial Intelligence Algorithms-- Patient-independent knowledge should be available (e.g. as “rules”)-- Patient-specific knowledge should be statistically extracted from the performance of each patient-- “Model field theory” approach to model relationships
CI user speech feature battery test Develop Optimized
Map for CI device Using:
- Fuzzy logic - Genetic Algorithms - Model Field Theory
Audiologist updates CI Map
Re-evaluate the CI user with adjusted map repeat until map is optimized
Speech feature to CI device map parameter knowledge base
Speech based Optimization of Cochlear Implant Processor patient Study
Benefits: -Optimized Map for CI user -Improved hearing performance -Improved quality of life -Reduced cost of tuning procedure
Perform standard CI user evaluation: -HINT -CNC Monitor
Session results
Confusion Error Matrix
Monitor Optimization results
CI user with optimized map
Initial Clinical Trial20 adults with
– N24 or New Freedom implants– Freedom ProcessorsAdjusted the following parameters
– Rate– Loudness growth– Frequency allocation tables Outcome measures
– CNC lists in quiet – BKB-SIN– Subjective questionnaire
Subject Demographics
Gender
Male Female
N=7N=13
Age (Years)
Mean S.D. Range
57.319.9
24-82
Length of CI Use (months)
Mean S.D. Range
25.628.975-115
Type of CI
N24 New Freedom
N=3N=17
Initial Clinical Trial
The Optimization program (Clarujust™) was designed to interface with a customized version of Cochlear Corp. Custom Sound so that programming changes recommended by the algorithm could be tested seamlessly. All stimuli were presented through a direct connection to the speech processor and at a constant level across all test sessions (approximately 60 dBA). 3 Sessions – two weeks apart
Clarujust™ A series of VCV syllables were presented & verbal responses were recorded by the researcher. NWE for the processor setting was calculatedThe next combination of FAT, PR & LG was automatically recommended & tested.Procedure was repeated for 30 minutes
AI Algorithm
Loudness Growth
Rate
FAT
Inputs from CI
software
DF Error Matrix
Learning feedback
Optimization Models
Procedures
Outcome measures Clarujust™ routineMap with lowest net weighted error (NWE) was selected and programmed in to processer for use until next session
Subject Map Parameters
250 500 720 900 1200 18000
2
4
6
8
RATE
NU
MB
ER
OF
S
UB
JE
CT
S
10 15 20 25 300
5
10
15
20
LOUDNESS GROWTH
NU
MB
ER
OF
S
UB
JE
CT
S
188-7938
188-7438
188-6938
188-6563
188-6063
188-5938
188-5813
0
5
10
15
20
Baseline Opt 1 Opt 2
FREQUENCY ALLOCATION TABLE (Hz)
NU
MB
ER
OF
S
UB
JE
CT
S
CNC Word Scores
-15
-5
5
15
25
35
45
55
65
75
85
95
-15
-5
5
15
25
35
45
55
65
75
85
95
Per
cen
t C
orr
ect
Subject Number
CNC Word Scores
Opt 1 gain
Opt 2 gain
Series3
Baseline
Opt 1
Opt 2
• Significant difference among the three conditions using Greenhouse-Geisser analysis (p < 0.004).
• Further trend analyses indicated a significant ascending omnibus trend from baseline (p < 0.004)
• Pairwise comparisons significant differences between baseline and Opt 1 (p < 0.025) and between Baseline and Opt 2 (p < 0.015).
CNC Phoneme Scores
-30
-10
10
30
50
70
90
110
130
150
170
190
210
230
250
270
290
-30
-10
10
30
50
70
90
110
130
150
170
190
210
230
250
270
290
118 106 110 114 101 103 116 102 120 121 104 119 113 112 105 109 108 107 117 115
Pho
nem
es c
orre
ct
Subject Number
CNC Phoneme Scores
Opt 1 gainOpt 2 gainSeries3
Baseline
Opt 1
Opt 2
•Significant difference among the three conditions using Greenhouse-Geisser analysis (p < 0.008).
•Further trend analyses indicated a significant ascending trend from baseline (p < 0.015)
•Pairwise comparisons showed significant differences between base line and Opt1 (p < 0.003) and between Baseline and Opt 2 (p < 0.04).
BKB-SIN Scores
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
114 106 120 118 110 101 102 103 104 121 119 113 108 105 107 116 112 109 117 115S
ign
al-t
o-N
ois
e R
atio
(S
NR
) in
dB
Subject Number
BKB-SIN Scores
Opt 1 gainOpt 2 gainSeries3
Baseline
Opt 1
Opt 2
• Significant difference among the three conditions using Greenhouse-Geisser analysis (p < 0.03).
• Further trend analyses indicated a significant ascending quadratic trend from baseline (p < 0.009).
• Pairwise comparisons showed significant differences between baseline and Opt 1 (p < 0.03)
Subjective Results
At the end of this clinical trial, 17 out of 20 patients preferred to continue using one of their optimized maps.
Subjective ratings in various situations were also obtained from each subject (Holden, et al, J Am Acad Audiol 18:777–793, 2007)
Subjective Performance in 19 Listening Situations
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
Conversation on the Telephone
Message on the Answering Machine
News on TV
Movies/Dramas/Sitcoms on TV
Radio in the Car
Radio at Home
Lyrics to Music
Conversation at Dinner Table
Conversation in Quiet with One
Conversation in Quiet with Several
Conversation in a Car
Conversation at Social Gathering
Conversation at Restaurant
Conversation with Cashier
Conversation with a Child
Conversation Outside
Someone in the Distance
Church Service
Meeting in a Large Room
Subjective Performance
Optimization 2
Optimization 1
Baseline
Summary
• The optimization method used in this study resulted in improved subject performance in all outcome measures.
• Speech perception was significantly better in word and phoneme identification with optimized maps.
• In addition, subjects performed better in noise using the optimized maps.
• Subjective tests suggest that patients preferred the optimized maps in their daily lives.
What is Next?
Continue to refine process with CI technologyCurrently doing clinical trials with two hearing aid manufacturers– Three pilot subjects have been fitted with bilateral
hearing aids using the optimization protocol
Future applications– Hybrids– Audiologic rehabilitation– Cell phones– ????
Thank you to the students involved
Hannah Siburt
Kevin Still
Elyse Schwartz
Bekah Gathercole
Acknowledgments
This project is funded by Audigence, Inc. and the Florida High Tech Corridor Council.
We wish to thank Cochlear Corporation for supplying the fitting software platform and for their extensive and timely technical support.
We also want to thank our subjects for their willingness to participate in the experiment.
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