Recognition of Spelled City Names in Automotive Environments

This contribution presents the development and evaluation of a spelled letter recognizer for automotive environments. Speech data which have been collected in real-world driving situations are used for the training of the hidden Markov models. The feature extraction is optimized with regard to the recognition accuracy. The best results for both arbitrary spelling sequences and constrained city name recognition are achieved by a system with two-channel LDA and integrated noise reduction.