  High-accuracy speech recognition is especially challenging when large
datasets are not available. It is possible to bridge this gap with careful and
knowledge-driven parsing combined with the biologically inspired CNN and the
learning guarantees of the Vapnik Chervonenkis (VC) theory. This work presents
a Shallow-CNN-HTSVM (Hierarchical Tree Support Vector Machine classifier)
architecture which uses a predefined knowledge-based set of rules with
statistical machine learning techniques. Here we show that gross errors present
even in state-of-the-art systems can be avoided and that an accurate acoustic
model can be built in a hierarchical fashion. The CNN-HTSVM acoustic model
outperforms traditional GMM-HMM models and the HTSVM structure outperforms a
MLP multi-class classifier. More importantly we isolate the performance of the
acoustic model and provide results on both the frame and phoneme level
considering the true robustness of the model. We show that even with a small
amount of data accurate and robust recognition rates can be obtained.
