Modelling of Speech Parameters of Punjabi by Pre-trained Deep Neural Network Using Stacked Denoising Autoencoders

Abstract

Statistical parametric speech synthesis (SPSS) techniques of speech synthesis such as deep neural network (DNN) and hidden markov model (HMM) have grown popularity since last decade over the concatenative speech synthesis approaches by modelling excitation and spectral parameters of speech to synthesize the waveforms from the written text. Due to inappropriate acoustic modelling, speech synthesized using HMM-based speech synthesis sounds muffled. DNN tried to improve the acoustic model by replacing decision trees in HMM with powerful regression model. Further, the performance of a deep neural network is greatly enhanced using pre-learning either restricted Boltzmann machines (RBM) or autoencoders. Although RBMs are capable to map multi-modal property of speech but results in spectral distortion of synthesized speech waveforms as non-consideration of reconstruction error. This article proposed the model of deep neural network which is pre-trained using stacked denoising autoencoders (SDA) to map speech parameters of Punjabi language. Denoising autoencoders work by adding noise in the training data and then reconstructing the original measurements to reduce the reconstruction error. The synthesized voice using the proposed model showed the VARN of 0.82, F0 RMSE (Hz) 9.03 and V/UV error rate of 4.04% have been observed.