METHOD AND APPARATUS FOR SPEECH ENDPOINT DETECTION BASED ON JOINTLY TRAINED DEEP NEURAL NETWORKS FOR COMBINING ACOUSTIC EMBEDDING WITH CONTEXT OF AUTOMATIC SPEECH RECOGNITION

摘要

A method and apparatus for detecting a voice endpoint based on a deep neural network that combines learning acoustic feature vector embedding and voice recognition context are presented. According to an embodiment, a method for detecting a voice endpoint based on a deep neural network may include inputting an acoustic feature vector sequence extracted from a microphone input signal to a first deep neural network (DNN) model and a second deep neural network model; And detecting a voice endpoint through a density layer by combining the hidden state of the last hidden layer of the first deep neural network model and the second deep neural network model.