arXiv (Cornell University) · 2018 · 201 citations · 6 references
ComputingComputer ScienceComputational LinguisticsMachine LearningNatural Language ProcessingDeep LearningLanguage ModelingPhoneme SegmentationSpeech RecognitionSpeech Recognition TestingChannelsPhoneme RecognitionRead SpeechSpeech Corpus AnnotationSpeech Data AnnotationSpeech Recognition EvaluationWord Segmentation
AISHELL-1 is by far the largest open-source speech corpus available for Mandarin speech recognition research. It was released with a baseline system containing solid training and testing pipelines for Mandarin ASR. In AISHELL-2, 1000 hours of clean read-speech data from iOS is published, which is free for academic usage. On top of AISHELL-2 corpus, an improved recipe is developed and released, containing key components for industrial applications, such as Chinese word segmentation, flexible vocabulary expension and phone set transformation etc. Pipelines support various state-of-the-art techniques, such as time-delayed neural networks and Lattic-Free MMI objective funciton. In addition, we also release dev and test data from other channels(Android and Mic). For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR. For industry, we hope AISHELL-2 recipe can be a helpful reference for building meaningful industrial systems and products.
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Hui Bu, Jiayu Du, Xingyu Na et al. · arXiv (Cornell University) · 2017 · 50 citations · Full text