Most conventional methods to detect glottal closure instants (GCI) are based on signal processing technologies and different GCI candidate selection methods. This paper proposes a classification method to detect glottal closure instants from speech waveforms using convolutional neural network (CNN). The procedure is divided into two successive steps. Firstly, a low-pass filtered signal is computed, whose negative peaks are taken as candidates for GCI placement. Secondly, a CNN-based classification model determines for each peak whether it corresponds to a GCI or not. The method is compared with three existing GCI detection algorithms on two publicly available databases. For the proposed method, the detection accuracy in terms of F1-score is 98.23%. Additional experiment indicates that the model can perform better after trained with the speech data from the speakers who are the same as those in the test set.

This paper proposes using tandem DBN approach-a hierarchical architecture that consists of two or more deep belief networks (DBNs) in tandem manner-for phoneme recognition task on TIMIT. First we describe the standard DBN approach applied in phoneme recognition and discuss the motivation of combining it with tandem classifier approach. We then perform series of experiments to find out the best configuration for the DBN in the second level and discover the full potential of this method. The experiments show that for the DBN in the second level, (a) 2048 units in each hidden layer is better than 1024 and 512 units, (b) for sufficient length of temporal context, two hidden layers are better, (c) the one gives best performance on development set shows 4% relative improvement on coretest set.

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