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Performance Evaluation of an Ambient Noise Clustering Method for Objective Speech Intelligibility Estimation
Author(s) -
Kobayashi Yosuke,
Kondo Kazuhiro
Publication year - 2014
Publication title -
electronics and communications in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 13
eISSN - 1942-9541
pISSN - 1942-9533
DOI - 10.1002/ecj.11609
Subject(s) - cluster analysis , intelligibility (philosophy) , rhyme , speech recognition , computer science , pattern recognition (psychology) , background noise , artificial intelligence , art , telecommunications , philosophy , literature , poetry , epistemology
SUMMARY We investigated the clustering of nonstationary noise used in the subjective and objective assessment of speech intelligibility. The feature vector used in the clustering comprises 15‐dimensional features used typically in MIR, and clustering was performed by the x ‐means method. We then conducted tests to validate the clustering results using the Japanese Diagnostic Rhyme Test. It was found that with the JEIDA‐NOISE database, the noise can be classified into three clusters, and significant differences in the speech intelligibility of the different clusters were seen. Finally, we tested the objective speech intelligibility assessment for each cluster using fwSNRseg and the logistic function. The performance of objective assessment was found to be improved by about 0.01 compared to the case without clustering.