Robust Recognition of Spontaneous Speech

This contribution describes the challenges and the progress which have been made in Verbmobil concerning robustness of speech recognition for various types of adverse conditions, like channel distortion, environmental noise and various speaker and speaking conditions. For the channel and noise problem classical approaches like cepstral bias normalization and spectral subtraction methods have been improved as well as new methods like parallel model combination. One major result is the fact, that an intelligent combination of various methods achieves the best results. Considerable progresses have also been made in research on unsupervised speaker adaptation. Several different main approaches are presented to improve robustness against variations of speaking rate, speaking style and speaker characteristics. The methods described include new estimation of the parameters for vocal tract length normalization, features and codebook transformation methods using ML algorithms, and pronunciation adaptation of the words in the lexicon.