Coping with Unseen Data Conditions: Investigating Neural Net Architectures, Robust Features, and Information Fusion for Robust Speech Recognition

The introduction of deep neural networks has significantly improved automatic speech recognition performance. For real world use, automatic speech recognition systems must cope with varying background conditions and unseen acoustic data. This work investigates the performance of traditional deep neural networks under varying acoustic conditions and evaluates their performance with speech recorded under realistic background conditions that are mismatched with respect to the training data. We explore using robust acoustic features, articulatory features, and traditional baseline features against both in-domain microphone channel-matched and channel-mismatched conditions as well as out-of-domain data recorded using far- and near-microphone setups containing both background noise and reverberation distortions. We investigate feature-combination techniques, both outside and inside the neural network, and explore neural-network-level combination at the output decision level. Results from this study indicate that robust features can significantly improve deep neural network performance under mismatched, noisy conditions, and that using multiple features reduces speech recognition error rates. Further, we observed that fusing multiple feature sets at the convolutional layer feature-map level was more effective than performing fusion at the input feature level or at the neural-network output decision level.