An Analysis of Deep Neural Networks in Broad Phonetic Classes for Noisy Speech Recognition

The introduction of Deep Neural Network (DNN) based acoustic models has produced dramatic improvements in performance. In particular, we have recently found that Deep Maxout Networks, a modification of DNNs’ feed-forward architecture that uses a max-out activation function, provides enhanced robustness to environmental noise. In this paper we further investigate how these improvements are translated into the different broad phonetic classes and how does it compare to classical Hidden Markov Models (HMM) based back-ends. Our experiments demonstrate that performance is still tightly related to the particular phonetic class being stops and affricates the least resilient but also that relative improvements of both DNN variants are distributed unevenly across those classes having the type of noise a significant influence on the distribution. A combination of the different systems DNN and classical HMM is also proposed to validate our hypothesis that the traditional GMM/HMM systems have a different type of error than the Deep Neural Networks hybrid models.