|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Modelling trajectories in statistical speech synthesis
If you have a question about this talk, please contact Kai Yu.
This is the second talk of the speech synthesis seminar series.
In statistical speech synthesis we build a probabilistic model of (processed) speech given (processed) text. The processed speech is in the form of a sequence of acoustic feature vectors, and the sequence over time of each component of this feature vector forms a trajectory. In this talk we’ll discuss how to model these trajectories.
We will first review a few ways in which the standard HMM synthesis model is unsatisfactory. In particular the standard model is unnormalized, and we’ll discuss the practical impact of this lack of normalization. We’ll then look at normalized approaches, including the trajectory HMM (a globally normalized model) and the autoregressive HMM (a locally normalized model). Finally we’ll discuss some other possible enhancements including minimum generation error (MGE) training.
This talk is part of the speech synthesis seminar series series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsMedical Genetics Graduate Student Meeting CU Explorer's Society Cancer Research UK Cambridge Institute Imaging Seminars
Other talksMicrobiota-host interplay in the intestine and its impact on health and disease Feeding the world without costing the eart: How can agriculture and biodiversity coexist? Fourth Rollo Davidson Lecture EXPRESSION OF FIBROBLAST ACTIVATING PROTEIN (FAP) IN TUMOUR ASSOCIATED FIBROBLASTS (TAF) OF CANINE MAST CELL TUMOURS AND CORRELATION WITH TUMOUR GRADE AND MITOTIC INDEX The hand of the naturalist: Charles Plumier, images and overseas natural history in late-17th-century France "History since the Sixties: from social science to the global turn"