Online Speech Recognition Using Multichannel Parallel Acoustic Score Computation and Deep Neural Network (DNN)- Based Voice-Activity Detector

Abstract

This paper aims to design an online, low-latency, and high-performance speech recognition system using a bidirectional long short-term memory (BLSTM) acoustic model. To achieve this, we adopt a server-client model and a context-sensitive-chunk-based approach. The speech recognition server manages a main thread and a decoder thread for each client and one worker thread. The main thread communicates with the connected client, extracts speech features, and buffers the features. The decoder thread performs speech recognition, including the proposed multichannel parallel acoustic score computation of a BLSTM acoustic model, the proposed deep neural network-based voice activity detector, and Viterbi decoding. The proposed acoustic score computation method estimates the acoustic scores of a context-sensitive-chunk BLSTM acoustic model for the batched speech features from concurrent clients, using the worker thread. The proposed deep neural network-based voice activity detector detects short pauses in the utterance to reduce response latency, while the user utters long sentences. From the experiments of Korean speech recognition, the number of concurrent clients is increased from 22 to 44 using the proposed acoustic score computation. When combined with the frame skipping method, the number is further increased up to 59 clients with a small accuracy degradation. Moreover, the average user-perceived latency is reduced from 11.71 s to 3.09–5.41 s by using the proposed deep neural network-based voice activity detector.