Machine learning improves human speech recognition

Date:
March 1, 2022
Source:
American Institute of Physics
Summary:
To understand how hearing loss impacts people, researchers study
people's ability to recognize speech, and hearing aid algorithms
are often used to improve human speech recognition. Researchers
explore a human speech recognition model based on machine learning
and deep neural networks.

They calculated how many words per sentence a listener understands
using automatic speech recognition. The study consisted of eight
normal-hearing and 20 hearing-impaired listeners who were exposed
to a variety of complex noises that mask the speech.



FULL STORY ========================================================================== Hearing loss is a rapidly growing area of scientific research as the
number of baby boomers dealing with hearing loss continues to increase
as they age.


==========================================================================
To understand how hearing loss impacts people, researchers study people's ability to recognize speech. It is more difficult for people to recognize
human speech if there is reverberation, some hearing impairment, or
significant background noise, such as traffic noise or multiple speakers.

As a result, hearing aid algorithms are often used to improve human
speech recognition. To evaluate such algorithms, researchers perform experiments that aim to determine the signal-to-noise ratio at which
a specific number of words (commonly 50%) are recognized. These tests,
however, are time- and cost- intensive.

In The Journal of the Acoustical Society of America, published by the Acoustical Society of America through AIP Publishing, researchers from
Germany explore a human speech recognition model based on machine learning
and deep neural networks.

"The novelty of our model is that it provides good predictions for
hearing- impaired listeners for noise types with very different complexity
and shows both low errors and high correlations with the measured data,"
said author Jana Rossbach, from Carl Von Ossietzky University.

The researchers calculated how many words per sentence a listener
understands using automatic speech recognition (ASR). Most people are
familiar with ASR through speech recognition tools like Alexa and Siri.

The study consisted of eight normal-hearing and 20 hearing-impaired
listeners who were exposed to a variety of complex noises that mask the
speech. The hearing-impaired listeners were categorized into three groups
with different levels of age-related hearing loss.

The model allowed the researchers to predict the human speech recognition performance of hearing-impaired listeners with different degrees of
hearing loss for a variety of noise maskers with increasing complexity in temporal modulation and similarity to real speech. The possible hearing
loss of a person could be considered individually.

"We were most surprised that the predictions worked well for all noise
types.

We expected the model to have problems when using a single competing
talker.

However, that was not the case," said Rossbach.

The model created predictions for single-ear hearing. Going forward,
the researchers will develop a binaural model since understanding speech
is impacted by two-ear hearing.

In addition to predicting speech intelligibility, the model could also potentially be used to predict listening effort or speech quality as
these topics are very related.

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dreams in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by American_Institute_of_Physics. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jana Rossbach, Birger Kollmeier, Bernd T. Meyer. A model of speech
recognition for hearing-impaired listeners based on deep
learning. The Journal of the Acoustical Society of America, 2022;
151 (3): 1417 DOI: 10.1121/10.0009411 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220301131051.htm

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