Comparison of DCT and autoencoder-based features for DNN-HMM multimodal silent speech recognition

Abstract

Hidden Markov Model and Deep Neural Network-Hidden Markov Model speech recognition performance for a portable ultrasound + video multimodal silent speech interface is investigated using Discrete Cosine Transform and Deep Auto Encoder-based features with a range of dimensionalities. Experimental results show that the two types of features achieve similar Word Error Rate, but that the autoencoder features maintain good performance even for very low-dimension feature vectors, demonstrating potential as a very compact representation of the information in multimodal silent speech data. It is also shown for the first time that the Deep Network/Markov approach, which has been demonstrated to be beneficial for acoustic speech recognition and for articulatory sensor-based silent speech, improves the silent speech recognition performance for video-based silent speech recognition as well.