Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging
Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging
End-to-end models that condition the output sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since distinct label histories correspond to distinct models states, such models are decoded using an approximate beam-search which produces a tree of hypotheses.In this work, …