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» A hierarchical point process model for speech recognition
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ICASSP
2008
IEEE
14 years 1 months ago
Unsupervised learning of auditory filter banks using non-negative matrix factorisation
Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. Th...
Alexander Bertrand, Kris Demuynck, Veronique Stout...
ICASSP
2009
IEEE
14 years 2 months ago
Extended VTS for noise-robust speech recognition
Model compensation is a standard way of improving the robustness of speech recognition systems to noise. A number of popular schemes are based on vector Taylor series (vts) compen...
Rogier C. van Dalen, Mark J. F. Gales
ICIP
2002
IEEE
14 years 9 months ago
Application of support vector machines classifiers to visual speech recognition
In this paper we proposed a visual speech recognition network based on Support Vector Machines. Each word of the dictionary is modeled by a set of temporal sequences of visemes. E...
Mihaela Gordan, Constantine Kotropoulos, Apostolos...
ICASSP
2010
IEEE
13 years 7 months ago
Synthesis of filled pauses based on a disfluent speech model
In the present paper we present a new approach to the synthesis of filled pauses. The problem is tackled from the point of view of disfluent speech synthesis. Based on the synth...
Jordi Adell, Antonio Bonafonte, David Escudero Man...
EMNLP
2008
13 years 9 months ago
Learning to Predict Code-Switching Points
Predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech ...
Thamar Solorio, Yang Liu