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MULTI-TASK TRAINING ARCHITECTURE AND STRATEGY FOR ATTENTION-BASED SPEECH RECOGNITION SYSTEM
MULTI-TASK TRAINING ARCHITECTURE AND STRATEGY FOR ATTENTION-BASED SPEECH RECOGNITION SYSTEM
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机译:基于注意的语音识别系统的多任务训练体系结构和策略
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摘要
Methods and apparatuses are provided for performing sequence to sequence (Seq2Seq) speech recognition training performed by at least one processor. The method includes acquiring a training set comprising a plurality of pairs of input data and target data corresponding to the input data, encoding the input data into a sequence of hidden states, performing a connectionist temporal classification (CTC) model training based on the sequence of hidden states, performing an attention model training based on the sequence of hidden states, and decoding the sequence of hidden states to generate target labels by independently performing the CTC model training and the attention model training.
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