In this work, we are interested in boosting speech attribute detection by formulating it as a multi-label classification task, and deep neural networks (DNNs) are used to design speech attribute detectors. A straightforward way to tackle the speech attribute detection task is to estimate DNN parameters using the mean squared error (MSE) loss function and employ a sigmoid function in the DNN output nodes. A more principled way is nonetheless to incorporate the micro-F1 measure, which is a widely used metric in the multi-label classification, into the DNN loss function to directly improve the metric of interest at training time. Micro-F1 is not differentiable, yet we overcome such a problem by casting our task under the maximal figure-of-merit (MFoM) learning framework. The results demonstrate that our MFoM approach consistently outperforms the baseline systems.

DEEP LEARNING WITH MAXIMAL FIGURE-OF-MERIT COST TO ADVANCE MULTI-LABEL SPEECH ATTRIBUTE DETECTION

SINISCALCHI, SABATO MARCO;
2017

Abstract

In this work, we are interested in boosting speech attribute detection by formulating it as a multi-label classification task, and deep neural networks (DNNs) are used to design speech attribute detectors. A straightforward way to tackle the speech attribute detection task is to estimate DNN parameters using the mean squared error (MSE) loss function and employ a sigmoid function in the DNN output nodes. A more principled way is nonetheless to incorporate the micro-F1 measure, which is a widely used metric in the multi-label classification, into the DNN loss function to directly improve the metric of interest at training time. Micro-F1 is not differentiable, yet we overcome such a problem by casting our task under the maximal figure-of-merit (MFoM) learning framework. The results demonstrate that our MFoM approach consistently outperforms the baseline systems.
978-1-5090-4903-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11387/123684
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