TY - GEN
T1 - Interpretable phonological features for clinical applications
AU - Jiao, Yishan
AU - Berisha, Visar
AU - Liss, Julie
N1 - Funding Information:
This work was partially supported by an NIH 1R21DC013812 grant. The authors graciously acknowledge a hardware donation from NVIDIA.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Instrumental analysis of speech sometimes complements subjective evaluations in speech and language therapy; however, apart from elemental speech features such as pitch and formant statistics, higher dimensional spectral features are rarely used in practice because they are clinically uninterpretable. While these features are likely to somehow be related to clinical intervention, this relationship remains to be determined. This paper uses artificial recurrent neural networks to map high-dimensional spectral features into phonological features that are easily interpretable and provide fine-resolution information regarding articulation quality. The evaluation on a dysarthric speech data set shows strong correlation between the phonological feature measures and perceptual ratings. To increase clinical utility, we provide a new way to visualize phonological disturbances that provides clinicians with actionable information about intervention strategies.
AB - Instrumental analysis of speech sometimes complements subjective evaluations in speech and language therapy; however, apart from elemental speech features such as pitch and formant statistics, higher dimensional spectral features are rarely used in practice because they are clinically uninterpretable. While these features are likely to somehow be related to clinical intervention, this relationship remains to be determined. This paper uses artificial recurrent neural networks to map high-dimensional spectral features into phonological features that are easily interpretable and provide fine-resolution information regarding articulation quality. The evaluation on a dysarthric speech data set shows strong correlation between the phonological feature measures and perceptual ratings. To increase clinical utility, we provide a new way to visualize phonological disturbances that provides clinicians with actionable information about intervention strategies.
KW - clinical applications
KW - phonological features
KW - recurrent neural networks
UR - http://www.scopus.com/inward/record.url?scp=85023740681&partnerID=8YFLogxK
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U2 - 10.1109/ICASSP.2017.7953117
DO - 10.1109/ICASSP.2017.7953117
M3 - Conference contribution
AN - SCOPUS:85023740681
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5045
EP - 5049
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -