Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT). Most of the existing MDD approaches focus on dealing with categorical errors (viz. one canonical phone is substituted by another one, aside from those mispronunciations caused by deletions or insertions). However, accurate detection and diagnosis of non-categorial or distortion errors (viz. approximating L2 phones with L1 (first-language) phones, or erroneous pronunciations in between) still seems out of reach. In view of this, we propose to conduct MDD with a novel endto-end automatic speech recognition (E2E-based ASR) approach. In particular, we expand the original L2 phone set with their corresponding anti-phone set, making the E2E-based MDD approach have a better capability to take in both categorical and non-categorial mispronunciations, aiming to provide better mispronunciation detection and diagnosis feedback. Furthermore, a novel transfer-learning paradigm is devised to obtain the initial model estimate of the E2E-based MDD system without resource to any phonological rules. Extensive sets of experimental results on the L2-ARCTIC dataset show that our best system can outperform the existing E2E baseline system and pronunciation scoring based method (GOP) in terms of the F1-score, by 11.05% and 27.71%, respectively.

Developments of noise robustness techniques are vital to the success of automatic speech recognition (ASR) systems in face of varying sources of environmental interference. Recent studies have shown that exploring low-dimensional structures of speech features can yield good robustness. Along this vein, research on low-rank representation (LRR), which considers the intrinsic structures of speech features lying on some low dimensional subspaces, has gained considerable interest from the ASR community. When speech features are contaminated with various types of environmental noise, its corresponding modulation spectra can be regarded as superpositions of unstructured sparse noise over the inherent linguistic information. As such, we in this paper endeavor to explore the low dimensional structures of modulation spectra, in the hope to obtain more noise-robust speech features. The main contribution is that we propose a novel use of the LRR-based method to discover the subspace structures of modulation spectra, thereby alleviating the negative effects of noise interference. Furthermore, we also extensively compare our approach with several well-practiced feature-based normalization methods. All experiments were conducted and verified on the Aurora-4 database and task. The empirical results show that the proposed LRR-based method can provide significant word error reductions for a typical DNN-HMM hybrid ASR system.

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Research the subprogram characteristics, combined with the actual production, introduce the subprogram application systematically, Illustrate flat milling, layer cutting, removal of residual material, realization of rough and finish processing, optimization of parts program, processing the same profiles and the same parts, etc., elucidate the method and technique of subprogramming, show the incremental programming of rotate instructions.