A novel privacy-preserving speech recognition framework using bidirectional LSTM

Abstract

Utilizing speech as the transmission medium in Internet of things (IoTs) is an effective way to reduce latency while improving the efficiency of human-machine interaction. In the field of speech recognition, Recurrent Neural Network (RNN) has significant advantages to achieve accuracy improvement on speech recognition. However, some of RNN-based intelligence speech recognition applications are insufficient in the privacy-preserving of speech data, and others with privacy-preserving are time-consuming, especially about model training and speech recognition. Therefore, in this paper we propose a novel Privacy-preserving Speech Recognition framework using Bidirectional Long short-term memory neural network, namely PSRBL. On the one hand, PSRBL designs new functions to construct security activation functions by combing with an additive secret sharing protocol, namely a secure piecewise-linear Sigmoid and a secure piecewise-linear Tanh respectively, to achieve privacy-preserving of speech data during speech recognition process running on edge servers. On the other hand, in order to reduce the time spent on both the training and the recognition of the speech model while keeping high accuracy during speech recognition process, PSRBL first utilizes secure activation functions to refit original activation functions in the bidirectional Long Short-Term Memory neural network (LSTM), and then makes full use of the left and the right context information of speech data by employing bidirectional LSTM. Experiments conducted on the speech dataset TIMIT show that our framework PSRBL performs well. Specifically compared with the state-of-the-art ones, PSRBL significantly reduces the time consumption on both the training and the recognition of the speech model under the premise that PSRBL and the comparisons are consistent in the privacy-preserving of speech data.