Robust Parallel Speech Recognition in Multiple Energy Bands

In this paper we will investigate the performance of TRAP-features on clean and noisy data. Multiple feature sets are evaluated on a corpus which was recorded in clean and noisy environment. In addition, the clean version was reverberated artificially. The feature sets are assembled from selected energy bands. In this manner multiple recognizers are trained using different energy bands. The outputs of all recognizers are joined with ROVER in order to achieve a single recognition result. This system is compared to a baseline recognizer that uses Mel frequency cepstrum coefficients (MFCC). In this paper we will point out that the use of artificial reverberation leads to more robustness to noise in general. Furthermore most TRAP-based features excel in phone recognition. While MFCC features prove to be better in a matched training/test situation, TRAP-features clearly outperform them in a mismatched training/test situation: When we train on clean data and evaluate on noisy data the word accuracy (WA) can be raised by 173 % relative (from 12.0 % to 32.8 % WA).