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Low-Resource Contextual Topic Identification on Speech ...
Liu, Chunxi
;
Wiesner, Matthew
;
Watanabe, Shinji
;
Harman, Craig
;
Trmal, Jan
;
Dehak, Najim
;
Khudanpur, Sanjeev
. - : arXiv, 2018
Abstract:
In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is independently classified. We first present a general purpose method for topic ID on spoken segments in low-resource languages, using a cascade of universal acoustic modeling, translation lexicons to English, and English-language topic classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large improvements. In particular, we propose an attention-based contextual model which is able to leverage the contexts in a selective manner. We test both our contextual and non-contextual models on four LORELEI languages, and on all but one our attention-based contextual model significantly outperforms the context-independent models. ... : Accepted for publication at 2018 IEEE Workshop on Spoken Language Technology (SLT) ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.1807.06204
https://arxiv.org/abs/1807.06204
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