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Formosa Speech Recognition Challenge 2018: Data, Plan and Baselines

机译:福尔摩沙语音识别挑战赛2018:数据,计划和基准

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This paper introduces the Formosa speech recognition (FSR) challenge 2018, presents the provided data profile, evaluation plan and reports the experimental results of the baseline systems. This challenge focuses on spontaneous Taiwanese Mandarin speech recognition (TMSR) and it is based on a real-life, multigene broadcast radio speech corpus, NER-Trs-Vol1, selected from the Formosa speech in the wild (FSW) project. To assist participants to establish a good starting system, a set of baseline systems were published based on various deep neural network (DNN) models. NER-Trs-Vol1 is free for participants (noncommercial license), and its corresponding Kaldi recipes for the baselines have been published online. Experimental results show that the combination of NER-Trs-Vol1 and Kaldi recipes is a good resource pack for spontaneous TMSR research and could be used to initialize an advanced semi-supervised training procedure to further improve the recognition performance.
机译:本文介绍了台塑语音识别(FSR)挑战2018,提出了提供的数据资料,评估计划并报告了基准系统的实验结果。这项挑战着重于自发的台湾普通话语音识别(TMSR),它基于从福尔摩沙野生语音(FSW)项目中选择的真实,多基因广播无线电语音语料库NER-Trs-Vol1。为了帮助参与者建立一个良好的启动系统,基于各种深度神经网络(DNN)模型发布了一组基准系统。 NER-Trs-Vol1对参与者免费(非商业许可),并且其相应的基线Kaldi配方已在线发布。实验结果表明,NER-Trs-Vol1和Kaldi配方的组合是自发TMSR研究的良好资源包,可用于初始化高级半监督训练程序,从而进一步提高识别性能。

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