Data-driven synthesis methods

Black and Hunt in fact used a linear regression technique, with features such as lexical stress, numbers of syllables between the current syllable and the end of the phrase, identity of the previous labels and so on. Once learned, the system is capable of generating a basic set of target points for any input, which we then interpolated and smoothed to produce the final FO contour. Other data driven techniques such as CART have proven suitable for S3mthesizing from AM representations [292], [340], 

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Prominence prediction by deterministic means is actually one of the most successful uses of non-statistical methods in speech synthesis. This can be attributed to a number of factors, for example the fact that the rules often don t interact or the fact that many of the rules are base on semantic features (such that even if we did use a data driven technique we would still have to come up with the semantic taxonomy by hand). Sproat notes [410] that statistical approaches have had only limited success as the issue (especially in compound noun phrases) is really one of breadth and not modelling regardless of how the prominence algorithm actually works, what it requires is a broad and exhaustive list of examples of compound nouns. Few complex generalisations are present (what machine learning algorithms are good at) and once presented with an example, the rules are not difficult to write by hand. 

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