Multi-class learning algorithm for deep neural network-based statistical parametric speech synthesis

• Published in: 2016 24th European Signal Processing Conference (EUSIPCO) pp. 1951 - 1955
• Main Authors: Eunwoo Song, Hong-Goo Kang
• Format: Conference Proceeding
• Language: English
• Published: EURASIP 01.08.2016
• Subjects: Acoustics; Clustering algorithms; Context; context clustering; deep neural network; Hidden Markov models; shared hidden layer; Signal processing algorithms; Speech; Statistical parametric speech synthesis; Training
• ISSN: 2076-1465
• DOI: 10.1109/EUSIPCO.2016.7760589

This paper proposes a multi-class learning (MCL) algorithm for a deep neural network (DNN)-based statistical parametric speech synthesis (SPSS) system. Although the DNN-based SPSS system improves the modeling accuracy of statistical parameters, its synthesized speech is often muffled because the training process only considers the global characteristics of the entire set of training data, but does not explicitly consider any local variations. We introduce a DNN-based context clustering algorithm that implicitly divides the training data into several classes, and train them via a shared hidden layer-based MCL algorithm. Since the proposed MCL method efficiently models both the universal and class-dependent characteristics of various phonetic information, it not only avoids the model over-fitting problem but also reduces the over-smoothing effect. Objective and subjective test results also verify that the proposed algorithm performs much better than the conventional method.