In this paper, we propose a discriminative dynamic Gaussian mixture selection (DGMS) strategy to generate reliable accent-specific units (ASUs) for multi-accent speech recognition. Time-aligned phone recognition is used to generate the ASUs that model accent variations explicitly and accurately. DGMS reconstructs and adjusts a pre-trained set of hidden Markov model (HMM) state densities to build dynamic observation densities for each input speech frame. A discriminative minimum classification error criterion is adopted to optimize the sizes of the HMM state observation densities with a genetic algorithm (GA). To the author's knowledge, the discriminative optimization for DGMS accomplishes discriminative training of discrete variables that is first proposed. We found the proposed framework is able to cover more multi-accent changes, thus reduce some performance loss in pruned beam search, without increasing the model size of the original acoustic model set. Evaluation on three typical Chinese accents, Chuan, Yue and Wu, shows that our approach outperforms traditional acoustic model reconstruction techniques with a syllable error rate reduction of 8.0%, 5.5% and 5.0%, respectively, while maintaining a good performance on standard Putonghua speech.

We propose a discriminative DGMS (dynamic Gaussian mixture selection) strategy to enhance restructuring of a pre-trained set of Gaussian mixture models to cover unexpected acoustic variations at run time in automatic speech recognition. The number of Gaussian components in each hidden Markov model (HMM) state set aside is determined by a minimum classification error criterion. We also use a genetic algorithm to solve the integer programing problem to find the globally optimal state size. This parameter is used to adjust the HMM state densities for each input speech frame, leading to both high robustness and good resolution for dynamic tracking to cover a diversity of temporal variations in speech. Tested on an accented speech recognition application, the proposed framework yields an improved syllable error rate reduction over the conventional DGMS and augmented HMM systems when evaluated on three typical Chinese accents, Chuan, Yue and Wu, while maintaining its performance for standard Putonghua.

We propose an attribute-based approach to accented speech recognition based on automatic speech attribute transcription with high efficiency detection of articulatory features. In order to utilize appropriate and extensible phonetic and linguistic knowledge, conditional random field (CRF) is designed to take frame-level inputs with binary feature functions. The use of CRF with merely the state features to generate probabilistic phone lattices is then utilized to solve the phone under-generation problem. Finally an attribute discrimination module is incorporated to handle a diversity of accent changes without retraining any model, leading to flexible “plug `n' play” modular design. The effectiveness of the proposed approach is evaluated on three typical Chinese accents, namely Guanhua, Yue and Wu. Our method yields a significant absolute phone recognition accuracy improvement 5.04%, 4.68% and 6.06% for the corresponding three accent types over a conventional monophone HMM system. Compared to a context-dependent triphone HMM system, we achieve comparable phone accuracies at only less than 20% of the computation cost. In addition, our proposed method is equally applicable to speaker-independent systems handling multiple accents.

Multiple accents are often present in Mandarin speech, as most Chinese have learned Mandarin as a second language. We propose generating reliable accent specific unit together with dynamic Gaussian mixture selection for multi-accent speech recognition. Time alignment phoneme recognition is used to generate such unit and to model accent variations explicitly and accurately. Dynamic Gaussian mixture selection scheme builds a dynamical observation density for each specified frame in decoding, and leads to use Gaussian mixture component efficiently. This method increases the covering ability for a diversity of accent variations in multi-accent, and alleviates the performance degradation caused by pruned beam search without augmenting the model size. The effectiveness of this approach is evaluated on three typical Chinese accents Chuan, Yue and Wu. Our approach outperforms traditional acoustic model reconstruction approach significantly by 6.30%, 4.93% and 5.53%, respectively on Syllable Error Rate (SER) reduction, without degrading on standard speech.

The Query-by-Humming (QBH) system allows users to retrieve songs by singing/humming. In this paper we propose a phraselevel piecewise linear scaling algorithm for melody match. Musical phrase boundaries are predicted for the query to split it to phrases. The boundaries of melody fragment corresponding to each phrase are allowed for adjusting in a limited scope. The algorithm employs Dynamic Programming and Recursive Alignment to search for the minimal piecewise matching cost upon Linear Scaling at phrase-level. Our experimental results on 5223 melody database show that the proposed algorithm outperforms traditional algorithms. The proposed algorithm gives significant improvements of 17.0%, 14.7% and 4.8% with respect to Linear Scaling, Dynamic Time Wrapping and Recursive Alignment in top-1 rate, respectively. The results show that the proposed algorithm is more efficient than the previous algorithms.

To achieve a good balance between matching accuracy and computation efficiency is a key challenge for query-by-humming (QBH) system. In this paper, we propose an approach of n-gram based fast match. Our n-gram method uses a robust statistical note transcription as well as error compensation method based on the analysis of frequent transcription errors. The effectiveness of our approach has been evaluated on a relatively large melody database with 5223 melodies. The experimental results show that when the searching space was reduced to only 10% of the whole size, 90% of the target melodies were preserved in the candidates, and 88% of the match accuracy of system was kept. Meanwhile, no obvious additional computation was applied.