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.

本研究提出了一种基于子空间表示的新的学习机制，可用于从话语中提取隐藏的音位结构以进行语言验证和方言 / 口音识别，并通过核机器（如支持向量机和基于子空间的神经网络）的子空间学习实现。