AP17-OLR Challenge: Data, Plan, and Baseline

2017-06-28 · Zhiyuan Tang, Dong Wang, Yixiang Chen, Qing Chen

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.

📄 PDF Abstract BibTeX arXiv:1706.09742

Code (1)

Rithmax/Sub-band-Envelope-Features-Using-Frequency-Domain-Linear-Prediction

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Decoder

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Diversity

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Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning

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Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is important for real-world verification settings, …

Explanation Generation Binary Classification Image Classification Semantic Similarity