Study of senone-based deep neural network approaches for spoken language recognition

This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR). These approaches have recently been found to outperform various baseline systems on different datasets, but they have not yet been co...

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Detalles Bibliográficos
Autores principales: Ferrer, L., Lei, Y., McLaren, M., Scheffer, N.
Formato: JOUR
Materias:
Deep neural networks (DNNs)
Senones
Spoken language recognition (SLR)
Forecasting
Gaussian distribution
Speech recognition
Vectors
Bottleneck features
Deep neural networks
Gaussian Mixture Model
Language identification
Language recognition
Score-level fusion
Spoken language recognition
Computational linguistics
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_23299290_v24_n1_p105_Ferrer
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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