At the time of beginning this thesis, statistical parametric speech synthesis (SPSS)
using hidden Markov models (HMMs) was the dominant synthesis paradigm within the
research community. SPSS systems are effective at generalising across the linguistic
contexts present in training data to account for inevitable unseen linguistic contexts at
synthesis-time, making these systems flexible and their performance stable. However
HMM synthesis suffers from a ‘ceiling effect’ in the naturalness achieved, meaning
that, despite great progress, the speech output is rarely confused for natural speech.
There are many hypotheses for the causes of reduced synthesis quality, and subsequent
required improvements, for HMM speech synthesis in literature. However, until this
thesis, these hypothesised causes were rarely tested.
This thesis makes two types of contributions to the field of speech synthesis; each
of these appears in a separate part of the thesis. Part I introduces a methodology for
testing hypothesised causes of limited quality within HMM speech synthesis systems.
This investigation aims to identify what causes these systems to fall short of natural
speech. Part II uses the findings from Part I of the thesis to make informed improvements
to speech synthesis.
The usual approach taken to improve synthesis systems is to attribute reduced synthesis
quality to a hypothesised cause. A new system is then constructed with the aim
of removing that hypothesised cause. However this is typically done without prior testing
to verify the hypothesised cause of reduced quality. As such, even if improvements
in synthesis quality are observed, there is no knowledge of whether a real underlying
issue has been fixed or if a more minor issue has been fixed. In contrast, I perform a
wide range of perceptual tests in Part I of the thesis to discover what the real underlying
causes of reduced quality in HMM synthesis are and the level to which they contribute.
Using the knowledge gained in Part I of the thesis, Part II then looks to make improvements
to synthesis quality. Two well-motivated improvements to standard HMM
synthesis are investigated. The first of these improvements follows on from averaging
across differing linguistic contexts being identified as a major contributing factor to
reduced synthesis quality. This is a practice typically performed during decision tree
regression in HMM synthesis. Therefore a system which removes averaging across
differing linguistic contexts and instead performs averaging only across matching linguistic
contexts (called rich-context synthesis) is investigated. The second of the motivated
improvements follows the finding that the parametrisation (i.e., vocoding) of
speech, standard practice in SPSS, introduces a noticeable drop in quality before any
modelling is even performed. Therefore the hybrid synthesis paradigm is investigated.
These systems aim to remove the effect of vocoding by using SPSS to inform the selection
of units in a unit selection system. Both of the motivated improvements applied
in Part II are found to make significant gains in synthesis quality, demonstrating the
benefit of performing the style of perceptual testing conducted in the thesis