Abstract
Lipreading is to recognize what the speakers say by the movement of lip only. Most of the previous works are to solve the problem of lipreading in English. For Mandarin lipreading, there are a few researches due to the lack of datasets. For that reason, we introduce a simple method here to build a dataset for sentence-level Mandarin lipreading from programs like news, speech and talk show. We use Hanyu Pinyin (a phonemic transcription of Chinese) as label and totally have 349 classes, while the number of Chinese characters is 1705 in our dataset. Therefore, for lipreading, there are two steps. The first step is to obtain the Hanyu Pinyin sequence. We propose a model that is composed of a 3D convolutional layer with DenseNet and residual bidirectional long short-term memory. After this, in order to get the final Chinese characters results, a model with a stack of multi-head attention is applied to convert Hanyu Pinyin into Chinese characters.
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