Efficient joint compensation of speech for the effects of additive noise and linear filtering

The authors describe two algorithms that provide robustness for automatic speech recognition systems in a fashion that is suitable for real-time environmental normalization for workstations of moderate size. The first algorithm is a modification of the SNR-dependent cepstral normalization (SDCN) and the fixed code-word dependent cepstral normalization (FCDCN) algorithms given by Acero and Stern (1990), except that unlike these algorithms it provides computationally-efficient environment normalization without prior knowledge of the acoustical characteristics of the environment in which the system will be operated. The second algorithm is a modification of the more complex CDCN algorithm that enables it to perform environmental compensation in better than real time. The authors compare the recognition accuracy, computational complexity, and amount of training data needed to adapt to new acoustical environments using these algorithms with several different types of headset-mounted and desktop microphones