Spoken language recognition based on senone posteriors
This paper explores in depth a recently proposed approach to spoken language recognition based on the estimated posteriors for a set of senones representing the phonetic space of one or more languages. A neural network (NN) is trained to estimate the posterior probabilities for the senones at a fram...
Guardado en:
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2014
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2308457X_v_n_p2150_Ferrer http://hdl.handle.net/20.500.12110/paper_2308457X_v_n_p2150_Ferrer |
| Aporte de: |
| Sumario: | This paper explores in depth a recently proposed approach to spoken language recognition based on the estimated posteriors for a set of senones representing the phonetic space of one or more languages. A neural network (NN) is trained to estimate the posterior probabilities for the senones at a frame level. A feature vector is then derived for every sample using these posteriors. The effect of the language used in training the NN and the number of senones are studied. Speech-activity detection (SAD) and dimensionality reduction approaches are also explored and Gaussian and NN backends are compared. Results are presented on heavily degraded speech data. The proposed system is shown to give over 40% relative gain compared to a state-of-the-art language recognition system at sample durations from 3 to 120 seconds. Copyright © 2014 ISCA. |
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