End-to-end language diarization for bilingual code-switching speech
Hexin Liu (Nanyang Technological University)
Leibny Paola Garcia Perera (Johns Hopkins University)
Xinyi Zhang (Nanyang Technological University)
J. Dauwels (TU Delft - Signal Processing Systems)
Andy Khong (Nanyang Technological University)
Sanjeev Khudanpur (Johns Hopkins University)
Suzy J. Styles (Nanyang Technological University)
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Abstract
We propose two end-to-end neural configurations for language diarization on bilingual code-switching speech. The first, a BLSTM-E2E architecture, includes a set of stacked bidirectional LSTMs to compute embeddings and incorporates the deep clustering loss to enforce grouping of languages belonging to the same class. The second, an XSA-E2E architecture, is based on an x-vector model followed by a self-attention encoder. The former encodes frame-level features into segmentlevel embeddings while the latter considers all those embeddings to generate a sequence of segment-level language labels. We evaluated the proposed methods on the dataset obtained from the shared task B in WSTCSMC 2020 and our handcrafted simulated data from the SEAME dataset. Experimental results show that our proposed XSA-E2E architecture achieved a relative improvement of 12.1% in equal error rate and a 7.4% relative improvement on accuracy compared with the baseline algorithm in the WSTCSMC 2020 dataset. Our proposed XSA-E2E architecture achieved an accuracy of 89.84% with a baseline of 85.60% on the simulated data derived from the SEAME dataset.