Context-Dependent MLPs for LVCSR: TANDEM, Hybrid or Both?

Gaussian Mixture Model (GMM) and Multi Layer Perceptron (MLP) based acoustic models are compared on a French large vocabulary continuous speech recognition (LVCSR) task. In addition to optimizing the output layer size of the MLP, the effect of the deep neural network structure is also investigated. Moreover, using different linear transformations (time derivatives, LDA, CMLLR) on conventional MFCC, the study is also extended to MLP based probabilistic and bottle-neck TANDEM features. Results show that using either the hybrid or bottleneck TANDEM approach leads to similar recognition performance. However, the best performance is achieved when deep MLP acoustic models are trained on concatenated cepstral and context-dependent bottle-neck features. Further experiments reveal the importance of the neighbouring frames in case of MLP based modeling, and that its gain over GMM acoustic models is strongly reduced by more complex features.