Unsupervised visualization of Under-resourced speech prosody

In this paper, an unsupervised visualization framework for analyzing under-resourced speech prosody is proposed. An experiment was carried out for Ibibio–a Lower Cross Language of the New Benue Congo family, spoken in the Southeast coastal region of Nigeria, West Africa. The proposed methodology adopts machine learning, with semi-automated procedure for extracting prosodic features from a translated prosodically stable corpus ‘The Tiger and the Mouse’—a text corpus that demonstrates the prosody of read-aloud English. A self-organizing map (SOM) was used to learn the classification of certain input vectors (speech duration, fundamental frequency: F0, phoneme pattern (vowels only), tone pattern), and provide visualization of the clusters structure. Results obtained from the experiment showed that duration and F0 features realized from speech syllables are indispensable for modeling phoneme and tone patterns, but the tone input classes revealed clusters with well separated boundaries and well distributed component planes, compared to the phoneme input classes. Further, except for very few outliers, the map weights were well distributed with proper neighboring neuron connections across the input space. A possible future work to advance this research is the development of the language's corpus, for the discovery of prosodic patterns in expressive speech.