Unsupervised mining of under-resourced speech corpora for tone features classification

Abstract

In this contribution, the unsupervised mining of speech corpora for the efficient classification of tone features was investigated. Input vectors to the experiment were generated from tone pattern alignments of Ibibio (Benue-Congo, Nigeria) corpus. The corpus used for the experiment contained 16,905 words/phrases. The proposed system design is novel, and integrates two unsupervised tools - k-means clustering and self organizing map (SOM) model, into a methodological workflow, that evaluates and selects the optimal number of clusters with the subsequent association of each clustering point to the input data points. In order to reduce data dimensionality for effective visualization, a non-negative matrix factorization (NMF) was introduced to rid the k-means clusters of noisy attributes. The k-means cluster points generated by the optimum clusters (two in this case) were evaluated by the Silhouette algorithm and finally fed into the SOM, to improve the efficiency of features classification. Results obtained validate existing research claims and demonstrates the importance of vowel-only features in the recognition of tone patterns. A SOM visualization of the input vectors revealed that vowel-only feature correlates better with other input vectors such as syllable and phoneme, compared to consonant-only features. Furthermore, clustering the input datasets into the optimal number of clusters enabled proper and timely visualization of the map. This contribution is therefore vital for advancing future speech processing research on under-resourced languages.