Speech Recognition with Missing Data using Recurrent Neural Nets

In the `missing data' approach to improving the robustness of automatic speech recognition to added noise, an initial process identifies spectraltemporal regions which are dominated by the speech source. The remaining regions are considered to be `missing'. In this paper we develop a connectionist approach to the problem of adapting speech recognition to the missing data case, using Recurrent Neural Networks. In contrast to methods based on Hidden Markov Models, RNNs allow us to make use of long-term time constraints and to make the problems of classification with incomplete data and imputing missing values interact. We report encouraging results on an isolated digit recognition task.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: S. Parveen, P. Green
• Keywords: Automatic Speech Recognition, Isolated Digit Recognition, NIPS 2001, NIPS 2007, Speech Recognition