  In this study, we present an innovative technique for speaker adaptation in
order to improve the accuracy of segmentation with application to
unit-selection Text-To-Speech (TTS) systems. Unlike conventional techniques for
speaker adaptation, which attempt to improve the accuracy of the segmentation
using acoustic models that are more robust in the face of the speaker's
characteristics, we aim to use only context dependent characteristics
extrapolated with linguistic analysis techniques. In simple terms, we use the
intuitive idea that context dependent information is tightly correlated with
the related acoustic waveform. We propose a statistical model, which predicts
correcting values to reduce the systematic error produced by a state-of-the-art
Hidden Markov Model (HMM) based speech segmentation. Our approach consists of
two phases: (1) identifying context-dependent phonetic unit classes (for
instance, the class which identifies vowels as being the nucleus of
monosyllabic words); and (2) building a regression model that associates the
mean error value made by the ASR during the segmentation of a single speaker
corpus to each class. The success of the approach is evaluated by comparing the
corrected boundaries of units and the state-of-the-art HHM segmentation against
a reference alignment, which is supposed to be the optimal solution. In
conclusion, our work supplies a first analysis of a model sensitive to
speaker-dependent characteristics, robust to defective and noisy information,
and a very simple implementation which could be utilized as an alternative to
either more expensive speaker-adaptation systems or of numerous manual
correction sessions.
