Feature space visualization with spatial similarity maps for pathological speech data

Klumpp P, Vasquez Correa J, Haderlein T, Nöth E (2019)

Publication Type: Conference contribution

Publication year: 2019

Publisher: International Speech Communication Association

Pages Range: 3068-3072

Conference Proceedings Title: Proc. Interspeech 2019

Event location: Graz

DOI: 10.21437/Interspeech.2019-2080

Abstract

The feature vectors of a data set encode information about relations between speaker groups, clusters and outliers. Based on the assumption that these relations are conserved within the spatial properties of feature vectors, we introduce similarity maps to visualize consistencies and deviations in magnitude and orientation between two feature vectors. We also present an iterative approach to find subspaces of a high-dimensional feature space that encode information about predefined speaker clusters. The methods were evaluated with two different data sets, one from chronically hoarse speakers and a second one from Parkinson’s Disease patients and a healthy control group. The results showed that similarity maps provide a decent visualization of speaker groups and the spatial properties of their respective feature vectors. With the iterative optimization, it was possible to find features that show pronounced spatial differences between predefined clusters.

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How to cite

APA:

Klumpp, P., Vasquez Correa, J., Haderlein, T., & Nöth, E. (2019). Feature space visualization with spatial similarity maps for pathological speech data. In Gernot Kubin, Thomas Hain, Bjorn Schuller, Dina El Zarka, Petra Hodl (Eds.), Proc. Interspeech 2019 (pp. 3068-3072). Graz, AT: International Speech Communication Association.

MLA:

Klumpp, Philipp, et al. "Feature space visualization with spatial similarity maps for pathological speech data." Proceedings of the 20th Annual Conference of the International Speech Communication Association: Crossroads of Speech and Language, INTERSPEECH 2019, Graz Ed. Gernot Kubin, Thomas Hain, Bjorn Schuller, Dina El Zarka, Petra Hodl, International Speech Communication Association, 2019. 3068-3072.

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