DESIPHER_Speech Degradation as an Indicator of Physiological Degeneration in ALS
| Status: | Completed |
|---|---|
| Conditions: | Neurology |
| Therapuetic Areas: | Neurology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 4/17/2018 |
| Start Date: | January 1, 2016 |
| End Date: | December 31, 2017 |
DESIPHER Speech Degradation as an Indicator of Physiological Degeneration in ALS
A disease called Amyotrophic lateral sclerosis (or ALS), which leads to difficulty
swallowing, breathing, and movement, has been found to be higher for those serving in the
military than in the general population. There are approximately 4,200 Veterans with ALS and
roughly 1,000 new cases each year. When doctors attempt to determine the degree to which an
ALS patient is suffering from the disease, they apply tests that are "graded" by experts.
However, this approach to testing patients may not be very accurate. Researchers aim to use a
system called DESIPHER to "listen" to ALS patients and find speech mistakes related to their
condition. Researchers believe that, by detecting different types of errors, DESIPHER serves
as a new kind of indicator of medical problems such as difficulty breathing or swallowing,
without human "grading". This may also lead to a better system for automatically
understanding ALS patients' speech.
swallowing, breathing, and movement, has been found to be higher for those serving in the
military than in the general population. There are approximately 4,200 Veterans with ALS and
roughly 1,000 new cases each year. When doctors attempt to determine the degree to which an
ALS patient is suffering from the disease, they apply tests that are "graded" by experts.
However, this approach to testing patients may not be very accurate. Researchers aim to use a
system called DESIPHER to "listen" to ALS patients and find speech mistakes related to their
condition. Researchers believe that, by detecting different types of errors, DESIPHER serves
as a new kind of indicator of medical problems such as difficulty breathing or swallowing,
without human "grading". This may also lead to a better system for automatically
understanding ALS patients' speech.
In 2008, Amyotrophic lateral sclerosis (ALS) or Lou Gehrig's Disease became a presumptively
compensable (service connected) disease as the Institute of Medicine (IOM) Committee stated
an association between the development of ALS and military service. According to the IOM
report, military service increases life risk of ALS by 1.5 fold. There are approximately
4,200 Veterans with ALS and roughly 1,000 new cases each year. At the Tampa VA, since 2007,
there has been a consistent rise in the number of Veterans diagnosed and treated with ALS.
Most physiological assessments that are commonly used to determine the functional status of
patients with ALS require trained clinical personnel to administer and interpret the results.
The investigators propose to use automatic speech understanding and machine learning software
(DESIPHER) to: identify speech pathologies and use them to predict other aspects of
physiological degeneration associated with ALS (e.g., respiratory difficulty or inability to
swallow), and ultimately improve speech recognition for those with speech impairments. The
investigators expect this to improve the ability to appropriately identify and intervene when
Veterans with ALS are at risk of serious adverse medical issues such as respiratory failure
and aspiration. The investigators postulate that analyzing the overall divergence of
(impaired) speech, from a "normal" baseline, will prove to be more robust and a better marker
for involvement than others that have been proposed.
Specific research questions to be addressed by this study are: (1) Is it possible to train a
speech recognition system to adapt to increasingly more frequent language/speech errors of
particular types, to produce an accurate textual transcript that would be readable by an ALS
patient's caregiver or physician? (2) Are specific changes in physiological functioning:
Forced Vital Capacity, tongue strength, speech velocity, weight (loss), aspiration risk, or
psychological distress, reflected in different types of language/speech errors associated
with ALS?
By understanding how speech functioning correlates with the degree to which other biophysical
functioning has degraded, it is possible to apply a new, non-invasive measure for assessing
the functionality of an ALS patient. In addition, the features associated with speech
degradation it is possible to adapt existing speech recognition software to a patient's
speech as it evolves over time, so that the quality of life for patients may be improved
through conversation with a computer.
Respiratory failure is the main cause of morbidity and mortality in ALS patients. The
investigators expect that the method of analyzing speech will present an excellent biomarker
for respiratory function, as there is an expected increase in pauses during speech due to the
necessity of increased frequency of respirations, a decrease in loudness, and decreased
overall velocity of speech. A second major cause of death is aspiration. As the articular
muscles decline, the investigators expect to note a decrease in the clarity of speech. Speech
involvement often precedes swallowing involvement in ALS; thus, the investigators expect that
increasing "speech divergence" will indicate potential aspiration risk.
compensable (service connected) disease as the Institute of Medicine (IOM) Committee stated
an association between the development of ALS and military service. According to the IOM
report, military service increases life risk of ALS by 1.5 fold. There are approximately
4,200 Veterans with ALS and roughly 1,000 new cases each year. At the Tampa VA, since 2007,
there has been a consistent rise in the number of Veterans diagnosed and treated with ALS.
Most physiological assessments that are commonly used to determine the functional status of
patients with ALS require trained clinical personnel to administer and interpret the results.
The investigators propose to use automatic speech understanding and machine learning software
(DESIPHER) to: identify speech pathologies and use them to predict other aspects of
physiological degeneration associated with ALS (e.g., respiratory difficulty or inability to
swallow), and ultimately improve speech recognition for those with speech impairments. The
investigators expect this to improve the ability to appropriately identify and intervene when
Veterans with ALS are at risk of serious adverse medical issues such as respiratory failure
and aspiration. The investigators postulate that analyzing the overall divergence of
(impaired) speech, from a "normal" baseline, will prove to be more robust and a better marker
for involvement than others that have been proposed.
Specific research questions to be addressed by this study are: (1) Is it possible to train a
speech recognition system to adapt to increasingly more frequent language/speech errors of
particular types, to produce an accurate textual transcript that would be readable by an ALS
patient's caregiver or physician? (2) Are specific changes in physiological functioning:
Forced Vital Capacity, tongue strength, speech velocity, weight (loss), aspiration risk, or
psychological distress, reflected in different types of language/speech errors associated
with ALS?
By understanding how speech functioning correlates with the degree to which other biophysical
functioning has degraded, it is possible to apply a new, non-invasive measure for assessing
the functionality of an ALS patient. In addition, the features associated with speech
degradation it is possible to adapt existing speech recognition software to a patient's
speech as it evolves over time, so that the quality of life for patients may be improved
through conversation with a computer.
Respiratory failure is the main cause of morbidity and mortality in ALS patients. The
investigators expect that the method of analyzing speech will present an excellent biomarker
for respiratory function, as there is an expected increase in pauses during speech due to the
necessity of increased frequency of respirations, a decrease in loudness, and decreased
overall velocity of speech. A second major cause of death is aspiration. As the articular
muscles decline, the investigators expect to note a decrease in the clarity of speech. Speech
involvement often precedes swallowing involvement in ALS; thus, the investigators expect that
increasing "speech divergence" will indicate potential aspiration risk.
Inclusion Criteria:
- Veterans will be diagnosed with ALS
- Native speakers of U.S. English
- Will have bulbar involvement identified during initial ALS inpatient evaluation
- Forced vital capacity (FVC) of greater than 50% of the expected value for age
- An Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) Score of 40 or
greater
Exclusion Criteria:
- A diagnosis of dementia
- FVC less than 50%
- Inability to speak
- Or inability to follow directions
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