Robust in-car spelling recognition - a tandem BLSTM-HMM approach

Abstract

As an intuitive hands-free input modality automatic spelling recognition is especially useful for in-car human-machine interfaces.However, for today's speech recognition engines it is extremely challenging to cope with similar sounding spelling speech sequences in the presence of noises such as the driving noise inside a car.Thus, we propose a novel Tandem spelling recogniser, combining a Hidden Markov Model (HMM) with a discriminatively trained bidirectional Long Short-Term Memory (BLSTM) recurrent neural net.The BLSTM network captures long-range temporal dependencies to learn the properties of in-car noise, which makes the Tandem BLSTM-HMM robust with respect to speech signal disturbances at extremely low signal-to-noise ratios and mismatches between training and test noise conditions.Experiments considering various driving conditions reveal that our Tandem recogniser outperforms a conventional HMM by up to 33%.