Flexible feature extraction and HMM design for a hybrid distributed speech recognition system in noisy environments

Using the client device of a distributed speech recognizer usually implies the presence of background noise since most scenarios for distributed speech recognition (DSR) are situated in a non-office environment. Thus, the general task is to choose the most suitable feature extraction method for the given conditions. We present a hybrid speech recognition approach implemented for DSR that allows the choice of arbitrary feature vectors (regarding number and range of value) without changing the amount of data sent to the recognition engine. Experiments were carried out using mel-cepstrum and RASTA-PLP features on the AURORA database. Results show how the recognition performance under different noise conditions can be adjusted if the different features are combined, and that our hybrid approach to DSR has advantages that could not that easily be obtained with traditional DSR architectures.