|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
VOCAL TRACT MODELLING WITH RECURRENT NEURAL NETWORKS
Tina Burrows and Mahesan Niranjan
In this paper, the speech production system is modelled using the true glottal excitation as the source and a recurrent neural network to represent the vocal tract. The hidden nodes have multiple delays of one and two samples, making the network equivalent to a parallel formant synthesiser in the linear regions of the hidden node sigmoids. An ARX model identification is carried out to initialise the neural network parameters. These parameters are re-estimated in an analysis-by-synthesis framework to minimise the synthesis (output) error. Unlike other analysis-by-synthesis speech production models such as CELP, the source and filter in this approach are decoupled, enabling manipulation of the source time-scale to achieve high quality pitch changes.
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