Towards a hybrid learning approach to efficient tone pattern recognition

Abstract

Tone has remained an interesting puzzle to the development of language resources for African languages, mainly because its appearance (within a word) is not segmentally fixed. In this contribution, we begin by proposing a tone marking framework that intelligently tags an input corpus using a close-copy synthesis of tone-tags generated by a Hidden Markov Model (HMM) syllabifier. Next, we investigate the recognition of tone patterns by building a generic architecture that will serve diverse languages. The proposed architecture is a multi-layer feedforward neural network implementing the Levenberg-Marquardt backpropagation algorithm. The network consists of, (i) seventeen inputs describing the tone patterns of Ibibio (ISO 693-3: nic; Ethnologue: IBB), with training data captured from an input corpus of 16,905 phrases; (ii) a target class that learns tone recognition from a combination of the input tone patterns and boundary tone – an important feature used for intonation analysis. Results obtained showed that our tone marking model perfectly tagged the input corpus, except for phonemes with more than one diacritic marks. Concerning the recognition of tone patterns, we deduced from a confusion matrix that 93.1% of the tone patterns were correctly classified, while the remaining 6.9% of the patterns were misclassified. A greater chunk of the misclassified cases came from non-boundary tone information, which presence inhibits speech quality. The ROC curve also showed good classification of the training, testing and validation datasets. A future direction of this paper is the introduction of an unsupervised solution and additional tone-bearing information such as syllables and vowels, to improve the learning system; and a comparison of our approach with other methods.