TRUES

Abstract

Tone recognition has been a basic but important task for speech recognition and assessment of tonal languages, such as Mandarin Chinese. Most previously proposed approaches adopt a two-step approach where syllables within an utterance are identified via forced alignment first, and tone recognition using a variety of classifiers---such as neural networks, Gaussian mixture models (GMM), hidden Markov models (HMM), support vector machines (SVM)---is then performed on each segmented syllable to predict its tone. However, forced alignment does not always generate accurate syllable boundaries, leading to unstable voiced-unvoiced detection and deteriorating performance in tone recognition. Aiming to alleviate this problem, we propose a robust approach called Tone Recognition Using Extended Segments (TRUES) for HMM-based continuous tone recognition. The proposed approach extracts an unbroken pitch contour from a given utterance based on dynamic programming over time-domain acoustic features of average magnitude difference function (AMDF). The pitch contour of each syllable is then extended for tri-tone HMM modeling, such that the influence from inaccurate syllable boundaries is lessened. Our experimental results demonstrate that the proposed TRUES achieves 49.13% relative error rate reduction over that of the recently proposed supratone modeling, which is deemed the state of the art of tone recognition that outperforms several previously proposed approaches. The encouraging improvement demonstrates the effectiveness and robustness of the proposed TRUES, as well as the corresponding pitch determination algorithm which produces unbroken pitch contours.