Abstract:End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since unique label histories correspond to distinct models states, such models are decoded using an approximate beam-search process which produces a tree of hypotheses.In this work, we study the influence of the amount of label context on the model's accuracy, and its impact on the efficiency of the decoding process. W… Show more
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