Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition

• URL: http://arxiv.org/abs/2203.15576v1
• Date: Mon, 28 Mar 2022 07:01:45 GMT
• Title: Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition
• Authors: Hung-Shin Lee, Yu Tsao, Shyh-Kang Jeng, Hsin-Min Wang
• Abstract summary: We propose a new learning mechanism based on subspace-based representation. It can extract concealed phonotactic structures from utterances for language verification and dialect/accent identification. The proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC methods.
• Score: 27.268047798971473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose a new learning mechanism based on subspace-based representation, which can extract concealed phonotactic structures from utterances, for language verification and dialect/accent identification. The framework mainly involves two successive parts. The first part involves subspace construction. Specifically, it decodes each utterance into a sequence of vectors filled with phone-posteriors and transforms the vector sequence into a linear orthogonal subspace based on low-rank matrix factorization or dynamic linear modeling. The second part involves subspace learning based on kernel machines, such as support vector machines and the newly developed subspace-based neural networks (SNNs). The input layer of SNNs is specifically designed for the sample represented by subspaces. The topology ensures that the same output can be derived from identical subspaces by modifying the conventional feed-forward pass to fit the mathematical definition of subspace similarity. Evaluated on the "General LR" test of NIST LRE 2007, the proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC methods and the lattice-based PPR-LM method, respectively. Furthermore, on the dialect/accent identification task of NIST LRE 2009, the SNN-based system performed better than the aforementioned four baseline methods.

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