Abstract
Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric learning method to make predictions for out-of-sample speakers based on a probability distance measure to the speakers in the training set. This is in contrast to traditional learning methods that rely on Euclidean distances between individual instances. We evaluate the method on two pathological speech data sets with promising results.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5050-5054 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509041176 |
| DOIs | |
| State | Published - Jun 16 2017 |
| Event | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017 |
Other
| Other | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 |
|---|---|
| Country | United States |
| City | New Orleans |
| Period | 3/5/17 → 3/9/17 |
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Keywords
- Distribution regression
- divergence
- objective assessment
- speech pathology
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Objective assessment of pathological speech using distribution regression. / Tu, Ming; Berisha, Visar; Liss, Julie.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 5050-5054 7953118.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Objective assessment of pathological speech using distribution regression
AU - Tu, Ming
AU - Berisha, Visar
AU - Liss, Julie
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric learning method to make predictions for out-of-sample speakers based on a probability distance measure to the speakers in the training set. This is in contrast to traditional learning methods that rely on Euclidean distances between individual instances. We evaluate the method on two pathological speech data sets with promising results.
AB - Objective assessment of pathological speech is an important part of existing systems for automatic diagnosis and treatment of various speech disorders. In this paper, we propose a new regression method for this application. Rather than treating speech samples from each speaker as individual data instances, we treat each speaker's data as a probability distribution. We propose a simple non-parametric learning method to make predictions for out-of-sample speakers based on a probability distance measure to the speakers in the training set. This is in contrast to traditional learning methods that rely on Euclidean distances between individual instances. We evaluate the method on two pathological speech data sets with promising results.
KW - Distribution regression
KW - divergence
KW - objective assessment
KW - speech pathology
UR - http://www.scopus.com/inward/record.url?scp=85016309853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016309853&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7953118
DO - 10.1109/ICASSP.2017.7953118
M3 - Conference contribution
AN - SCOPUS:85016309853
SP - 5050
EP - 5054
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -