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.

This paper proposes a formant-based approach for computer-assisted English vowel assessment. Various studies in formant-based speech synthesis have suggested the importance of formant coefficients; this motivates us to investigate pronunciation assessment using formant information instead of MFCC (Mel-frequency cepstral coefficients) alone. In particular, we explore the multistream HMM with the addition of formant information to improve the phoneme segmentation. We then propose the use of PCN (pronunciation confusion network) together with a formant-based confidence measure to improve error detection rates. Furthermore, the pros and cons of using cross-word phone model for both native speakers and L2 learners are discussed. Experimental results demonstrate the feasibility of the proposed approach for automatic vowel pronunciation assessment. Index Terms: computer assisted pronunciation training, formant, assessment, pronunciation confusion network, speech recognition

This work describes the algorithms used in a prototypical software system for automatic pronunciation assessment of Mandarin Chinese. The system uses Viterbi decoding to isolate each syllable and find the log probability of a given utterance based on HMM (hidden Markov models). The isolated syllables are then sent to a GMM (Gaussian mixture model) for tone recognition. Based on the log probability and the result from tone recognition, a parametric scoring function, using a neural network, is constructed to approximate the scoring results from human experts. The experimental results demonstrate the system can consistently gives scores that are close to those from human's subjective evaluation.

This demo presents an implementation of a content-based music retrieval system that can take a user's acoustic input (8-second clip of singing or humming) via a microphone and then retrieve the intended song from a database containing 13,000 candidate songs. The system, known as Super MBox, demonstrates the feasibility of real-time music retrieval with a high recognition rate, which can be used for music search engines over the Internet and/or query engines in digital music libraries or karaoke machines.