Analysis with Autosegmental models

The biggest difficulty with the AM models is the difficulty in labelling corpora. Several studies have been conducted in labelling with ToBI with the general conclusion that while labellers can often identify which syllables bear pitch accents, they are very poor at agreeing on which particular 

Two major problems stem from this. Firstly, any database which has been labelled with ToBI will have a significant amount of noise associated with the pitch accent label classes. Secondly, for any large scale machine learning or data driven approach, we need a considerable amount of labelled data to the extent that it is impractical to label data by hand. As we shall see in Chapters 15 and 16, virtually all other aspects of a modem data driven TTS system s data are labelled automatically, and so it is a significant drawback if the intonation component can not be labelled automatically as well. Because however the level of human labeller agreement is so low, it is very hard to train a system successfully on these labels we can hardly expect an automatic algorithm to perform better than a human at such a task. 

One solution that is increasingly adopted is to forgo the distinction between label types altogether, see for instance [488]. While the break index and boundary tone components are often kept, only a single type of pitch accent is used in effect the labellers are marking whether a word is intonationally prominent or not. However, it should be clear that such an approach effectively reduces ToBI to a data driven system of the type described below. 

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