Use of the harmonic phase in synthetic speech detection

Special Session paper: recent PhD thesis descriptionThis PhD dissertation was written by Jon Sanchez and supervised by Inma Hernáez and Ibon Saratxaga. It was defended at the University of the Basque Country the 5th of February 2016. The committee members were Dr. Alfonso Ortega Giménez (UniZar), Dr. Daniel Erro Eslava (UPV/EHU) and Dr. Enric Monte Moreno (UPC). The dissertation was awarded a "sobresaliente cum laude” qualification.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with FEDER support (RESTORE project,TEC2015-67163-C2-1-R) and the Basque Government (ELKAROLA project, KK-2015/00098)

Use of the harmonic phase in synthetic speech detection

Special Session paper: recent PhD thesis descriptionThis PhD dissertation was written by Jon Sanchez and supervised by Inma Hernáez and Ibon Saratxaga. It was defended at the University of the Basque Country the 5th of February 2016. The committee members were Dr. Alfonso Ortega Giménez (UniZar), Dr. Daniel Erro Eslava (UPV/EHU) and Dr. Enric Monte Moreno (UPC). The dissertation was awarded a "sobresaliente cum laude” qualification.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness with FEDER support (RESTORE project,TEC2015-67163-C2-1-R) and the Basque Government (ELKAROLA project, KK-2015/00098)

LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices

Recent developments in speech synthesis have produced systems capable of outcome intelligible speech, but now researchers strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. HMM-based Speech Synthesis is of great interest to many researchers, due to its ability to produce sophisticated features with small footprint. Despite such progress, its quality has not yet reached the level of the predominant unit-selection approaches that choose and concatenate recordings of real speech. Recent efforts have been made in the direction of improving these systems. In this paper we present the application of Long-Short Term Memory Deep Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order to obtain closer spectral characteristics to those of natural speech. The results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure

An experimental comparison of multiple vocoder types

This paper presents an experimental comparison of a broad range of the leading vocoder types which have been previously described. We use a reference implementation of each of these to create stimuli for a listening test using copy synthesis. The listening test is performed using both Lombard and normal read speech stimuli, and with two types of question for comparison. Multi-dimensional Scaling (MDS) is conducted on the listener responses to analyse similarities in terms of quality between the vocoders. Our MDS and clustering results show that the vocoders which use a sinusoidal synthesis approach are perceptually distinguishable from the source-filter vocoders. To help further interpret the axes of the resulting MDS space, we test for correlations with standard acoustic quality metrics and find one axis is strongly correlated with PESQ scores. We also find both speech style and the format of the listening test question may influence test results. Finally, we also present preference test results which compare each vocoder with the natural speech