Robust feature extraction and uncertainty estimation based on attractor dynamics in cyclic deep denoising autoencoders

Because the input and the output values of the deep denoising autoencoders (DDAs) have the same representation space, the output values of a DDA can be used as its input values, which leads to a repeatable cycle for DDA. Using this fact, in this paper we proposed cyclic deep denoising autoencoders (CDDAs). Moreover, the proposed CDDA was exploited for noise robust bottleneck feature extraction. Considering the CDDA as an attractor network, we analyzed its attractor dynamics and proposed an uncertainty estimation method for the bottleneck features. The capabilities of the proposed feature extraction method are: eliminating the need for labeled training data, improving the robustness of bottleneck features of DDAs without excessive training, providing a new uncertainty estimation trend, and being applicable in other speech processing tasks like speaker recognition. Our proposed feature extraction method was evaluated on the Aurora-2 robust speech recognition task and compared it to conventional Mel frequency cepstral coefficients (MFCC) and autoencoder bottleneck features. The proposed method resulted in up to 8% improvement in word recognition accuracy relative to MFCC features.