Computer engineers and hearing
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise.
In the Journal of the Acoustical Society
of America , they describe how they used
the latest developments in neural
networks to boost test subjects'
recognition of spoken words from as
low as 10 percent to as high as 90
percent.
The researchers hope the technology will
pave the way for next-generation digital
hearing aids. Such hearing aids could
even reside inside smartphones; the
phones would do the computer
processing, and broadcast the enhanced
signal to ultra-small earpieces
wirelessly.
Several patents are pending on the
technology, and the researchers are
working with leading hearing aid
manufacturer Starkey, as well as others
around the world to develop the
technology.
Conquering background noise has been
a "holy grail" in hearing technology for
half a century, explained Eric Healy,
professor of speech and hearing science
and director of Ohio State's Speech
Psychoacoustics Laboratory.
The desire to understand one voice in
roomful of chatter has been dubbed the
"cocktail party problem."
"Focusing on what one person is saying
and ignoring the rest is something that
normal-hearing listeners are very good
at, and hearing-impaired listeners are
very bad at," Healy said. "We've come
up with a way to do the job for them,
and make their limitations moot."
Key to the technology is a computer
algorithm developed by DeLiang "Leon"
Wang, professor of computer science
and engineering, and his team. It quickly
analyzes speech and removes most of
the background noise.
Computer engineers and hearing
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise.
Researchers played this sound clip for
study participants to test whether they
could hear a single, clear sentence being
said amongst a background of babble.
Credit: The Ohio State University Speech
Psychoacoustics Laboratory
"For 50 years, researchers have tried to
pull out the speech from the background
noise. That hasn't worked, so we
decided to try a very different approach:
classify the noisy speech and retain only
the parts where speech dominates the
noise," Wang said.
In initial tests, Healy and doctoral
student Sarah Yoho removed twelve
hearing-impaired volunteers' hearing
aids, then played recordings of speech
obscured by background noise over
headphones. They asked the
participants to repeat the words they
heard. Then they re-performed the same
test, after processing the recordings
with the algorithm to remove
background noise.
Computer engineers and hearing
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise. In this
clip, a computer algorithm has removed
the background babble, so that a single,
clear sentence can be heard: "They ate
the lemon pie." Credit: The Ohio State
University Speech Psychoacoustics
Laboratory
They tested the algorithm's effectiveness
against "stationary noise"—a constant
noise like the hum of an air conditioner
—and then with the babble of other
voices in the background.
The algorithm was particularly affective
against background babble, improving
hearing-impaired people's
comprehension from 25 percent to close
to 85 percent on average. Against
stationary noise, the algorithm improved
comprehension from an average of 35
percent to 85 percent.
For comparison, the researchers
repeated the test with twelve
undergraduate Ohio State students who
were not hearing-impaired. They found
that scores for the normal-hearing
listeners without the aid of the
algorithm's processing were lower than
those for the hearing-impaired listeners
with processing..
"That means that hearing-impaired
people who had the benefit of this
algorithm could hear better than
students with no hearing loss," Healy
said.
A new $1.8 million grant from the
National Institutes of Health will support
the research team's refinement of the
algorithm and testing on human
volunteers.
The algorithm is unique, Wang said,
because it utilizes a technique called
machine learning. He and doctoral
student Yuxuan Wang are training the
algorithm to separate speech by
exposing it to different words in the
midst of background noise. They use a
special type of neural network called a
"deep neural network" to do the
processing—so named because its
learning is performed through a deep
layered structure inspired by the human
brain.
These initial tests focused on pre-
recorded sounds. In the future, the
researchers will refine the algorithm to
make it better able to process speech in
real time. They also believe that, as
hearing aid electronics continue to
shrink and smartphones become even
more common, phones will have more
than enough processing power to run
the algorithm and transmit sounds
instantly—and wirelessly—to the
listener's ears.
Some 10 percent of the population—700
million people worldwide—suffer from
hearing loss. The problem increases
with age. In a 2006 study , Healy
determined that around 40 percent of
people in their 80s experience hearing
loss that is severe enough to make
others' speech at least partially
unintelligible.
One of them is Wang's mother, who, like
most people with her condition, has
difficulty filtering out background noise .
"She's been one of my primary
motivations," Wang said. "When I go
visit her, she insists that only one person
at a time talk at the dinner table. If more
than one person talks at the same time,
she goes absolutely bananas because
she just can't understand. She's tried all
sorts of hearing aids, and none of them
works for this problem."
"This is the first time anyone in the
entire field has demonstrated a
solution," he continued. "We believe that
this is a breakthrough in the true sense
of the word."
The technology is currently being
commercialized and is available for
license from Ohio State's Technology
Commercialization and Knowledge
Transfer Office.
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise.
In the Journal of the Acoustical Society
of America , they describe how they used
the latest developments in neural
networks to boost test subjects'
recognition of spoken words from as
low as 10 percent to as high as 90
percent.
The researchers hope the technology will
pave the way for next-generation digital
hearing aids. Such hearing aids could
even reside inside smartphones; the
phones would do the computer
processing, and broadcast the enhanced
signal to ultra-small earpieces
wirelessly.
Several patents are pending on the
technology, and the researchers are
working with leading hearing aid
manufacturer Starkey, as well as others
around the world to develop the
technology.
Conquering background noise has been
a "holy grail" in hearing technology for
half a century, explained Eric Healy,
professor of speech and hearing science
and director of Ohio State's Speech
Psychoacoustics Laboratory.
The desire to understand one voice in
roomful of chatter has been dubbed the
"cocktail party problem."
"Focusing on what one person is saying
and ignoring the rest is something that
normal-hearing listeners are very good
at, and hearing-impaired listeners are
very bad at," Healy said. "We've come
up with a way to do the job for them,
and make their limitations moot."
Key to the technology is a computer
algorithm developed by DeLiang "Leon"
Wang, professor of computer science
and engineering, and his team. It quickly
analyzes speech and removes most of
the background noise.
Computer engineers and hearing
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise.
Researchers played this sound clip for
study participants to test whether they
could hear a single, clear sentence being
said amongst a background of babble.
Credit: The Ohio State University Speech
Psychoacoustics Laboratory
"For 50 years, researchers have tried to
pull out the speech from the background
noise. That hasn't worked, so we
decided to try a very different approach:
classify the noisy speech and retain only
the parts where speech dominates the
noise," Wang said.
In initial tests, Healy and doctoral
student Sarah Yoho removed twelve
hearing-impaired volunteers' hearing
aids, then played recordings of speech
obscured by background noise over
headphones. They asked the
participants to repeat the words they
heard. Then they re-performed the same
test, after processing the recordings
with the algorithm to remove
background noise.
Computer engineers and hearing
scientists at The Ohio State University
have made a potential breakthrough in
solving a 50-year-old problem in
hearing technology: how to help the
hearing-impaired understand speech in
the midst of background noise. In this
clip, a computer algorithm has removed
the background babble, so that a single,
clear sentence can be heard: "They ate
the lemon pie." Credit: The Ohio State
University Speech Psychoacoustics
Laboratory
They tested the algorithm's effectiveness
against "stationary noise"—a constant
noise like the hum of an air conditioner
—and then with the babble of other
voices in the background.
The algorithm was particularly affective
against background babble, improving
hearing-impaired people's
comprehension from 25 percent to close
to 85 percent on average. Against
stationary noise, the algorithm improved
comprehension from an average of 35
percent to 85 percent.
For comparison, the researchers
repeated the test with twelve
undergraduate Ohio State students who
were not hearing-impaired. They found
that scores for the normal-hearing
listeners without the aid of the
algorithm's processing were lower than
those for the hearing-impaired listeners
with processing..
"That means that hearing-impaired
people who had the benefit of this
algorithm could hear better than
students with no hearing loss," Healy
said.
A new $1.8 million grant from the
National Institutes of Health will support
the research team's refinement of the
algorithm and testing on human
volunteers.
The algorithm is unique, Wang said,
because it utilizes a technique called
machine learning. He and doctoral
student Yuxuan Wang are training the
algorithm to separate speech by
exposing it to different words in the
midst of background noise. They use a
special type of neural network called a
"deep neural network" to do the
processing—so named because its
learning is performed through a deep
layered structure inspired by the human
brain.
These initial tests focused on pre-
recorded sounds. In the future, the
researchers will refine the algorithm to
make it better able to process speech in
real time. They also believe that, as
hearing aid electronics continue to
shrink and smartphones become even
more common, phones will have more
than enough processing power to run
the algorithm and transmit sounds
instantly—and wirelessly—to the
listener's ears.
Some 10 percent of the population—700
million people worldwide—suffer from
hearing loss. The problem increases
with age. In a 2006 study , Healy
determined that around 40 percent of
people in their 80s experience hearing
loss that is severe enough to make
others' speech at least partially
unintelligible.
One of them is Wang's mother, who, like
most people with her condition, has
difficulty filtering out background noise .
"She's been one of my primary
motivations," Wang said. "When I go
visit her, she insists that only one person
at a time talk at the dinner table. If more
than one person talks at the same time,
she goes absolutely bananas because
she just can't understand. She's tried all
sorts of hearing aids, and none of them
works for this problem."
"This is the first time anyone in the
entire field has demonstrated a
solution," he continued. "We believe that
this is a breakthrough in the true sense
of the word."
The technology is currently being
commercialized and is available for
license from Ohio State's Technology
Commercialization and Knowledge
Transfer Office.
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