Investigating the shortcomings of HMM synthesis

This paper presents the beginnings of a framework for formal testing of the causes of the current limited quality of HMM (Hidden Markov Model) speech synthesis. This framework separates each of the effects of modelling to observe their independent effects on vocoded speech parameters in order to address the issues that are restricting the progression to highly intelligible and natural-sounding speech synthesis. The simulated HMM synthesis conditions are performed on spectral speech parameters and tested via a pairwise listening test, asking listeners to perform a “same or different ” judgement on the quality of the synthesised speech produced between these conditions. These responses are then processed using multidimensional scaling to identify the qualities in modelled speech that listeners are attending to and thus forms the basis of why they are distinguishable from natural speech. The future improvements to be made to the framework will finally be discussed which include the extension to more of the parameters modelled during speech synthesis

Two-pass decision tree construction for unsupervised adaptation of HMM-based synthesis models

Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to firstly estimate the transcription of the adaptation data. By defining a mapping between HMM-based synthesis models and ASR-style models, this paper introduces an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for supplementary acoustic models. Further, this enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data

Speech synthesis, Speech simulation and speech science

Speech synthesis research has been transformed in recent years through the exploitation of speech corpora - both for statistical modelling and as a source of signals for concatenative synthesis. This revolution in methodology and the new techniques it brings calls into question the received wisdom that better computer voice output will come from a better understanding of how humans produce speech. This paper discusses the relationship between this new technology of simulated speech and the traditional aims of speech science. The paper suggests that the goal of speech simulation frees engineers from inadequate linguistic and physiological descriptions of speech. But at the same time, it leaves speech scientists free to return to their proper goal of building a computational model of human speech production