AP17-OLR Challenge: Data, Plan, and Baseline
This work provides a standardized dataset and evaluation framework for researchers in multilingual speech recognition, though it is incremental as it builds on an existing challenge.
The paper introduces the AP17-OLR Challenge, which expands on a previous event by including more languages and focusing on short utterances for oriental language recognition, and reports baseline results using i-vector and neural network models to demonstrate the utility of the provided data.
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.