Adaptive beam pruning for automatic speech recognition

Abstract

A reduced latency system for automatic speech recognition (ASR). The system can use certain feature values describing the state of ASR processing to estimate how far a lowest scoring node for an audio frame is from a potential node likely be part of the Viterbi path. The system can then adjust its beam width in a manner likely to encompass the node likely to be on the Viterbi path, thus pruning unnecessary nodes and reducing latency. The feature values and estimated distances may be based on a set of training data, where the system identifies specific nodes on the Viterbi path and determines what feature values correspond to what desired beam widths. Trained models or other data may be created at training and used at runtime to dynamically adjust the beam width, as well as other settings such as threshold number of active nodes.