Pier Domenico Batzu

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This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers. The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment. The(More)
Bidirectional recurrent neural nets have demonstrated state-ofthe-art performance for parametric speech synthesis. In this paper, we introduce a top-down application of recurrent neural net models to unit-selection synthesis. A hierarchical cascaded network graph predicts context phone duration, speech unit encoding and frame-level logF0 information that(More)
This paper investigates the use of a Neural Network classifier for topic identification from conversational telephone speech, which exploits rich recognition results coming from an automatic speech recognizer. The baseline features used to feed the neural classifier are produced using the words extracted from the 1-best sequence. Rich recognition results(More)
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