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- W2918804987 abstract "In this paper, we propose a novel framework by combining the phonetic temporal neural model (PTN) with an improved LSTM (IM-LSTM). This is achieved by using an up-down connection from the time t to t+1 in the LSTM structure, which aims to capture the latent information from the previous time step. This updated structure can perform better to discriminate the frame-level phonetic information produced by PTN. On the AP16-OLR language identification dataset, our final model achieves relative growth rate 5.04%, 2.19%, 2.73% on EER and 6.55%, 5.81%, 2.23% on C <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>avg</sub> in 1s, 3s and full-length utterance condition than the standard PTN, respectively. The proposed framework receives a better performance than the standard PTN and other proposed models, particularly in 1s condition. This shows the efficacy and flexibility of the proposed method." @default.
- W2918804987 created "2019-03-11" @default.
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- W2918804987 date "2018-08-01" @default.
- W2918804987 modified "2023-10-09" @default.
- W2918804987 title "An Improved LSTM For Language Identification" @default.
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- W2918804987 doi "https://doi.org/10.1109/icsp.2018.8652445" @default.
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