Phonetic Temporal Neural Model for Language Identification

Tang, Zhiyuan; Wang, Dong; Chen, Yixiang; Li, Lantian; Abel, Andrew

doi:10.48550/arxiv.1705.03151

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B Dnn Systems1

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2017

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“…1 (a) and (b) respectively. The third one is based on the recently proposed phonetic temporal neural (PTN) model [22], where an auxiliary phonetic model produces phonetic feature, and an RNN LID model is used to identify the language. The architecture is shown in Fig.…”

Section: B Dnn Systemsmentioning

confidence: 99%

AP17-OLR challenge: Data, plan, and baseline

Tang

Wang

Chen

et al. 2017

2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR. Compared to the event last year (AP16-OLR), the new challenge involves more languages and focuses more on short utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two types of baselines are constructed to assist the participants, one is based on the i-vector model and the other is based on various neural networks. We report the baseline results evaluated with various metrics defined by the AP17-OLR evaluation plan and demonstrate that the combined database is a reasonable data resource for multilingual research. All the data is free for participants, and the Kaldi recipes for the baselines have been published online.

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Section: B Dnn Systemsmentioning

confidence: 99%