Abstract. In this article we describe methods for improving the RWTH German speech recognizer used within the VERBMOBIL project. In particular, we present acceleration methods for the search based on both within-word and across-word phoneme models. We also study incremental methods to reduce the response time of the online speech recognizer. Finally, we present experimental off-line results for the three VERBMOBIL scenarios. We report on word error rates and real-time factors for both speaker independent and speaker dependent recognition. 1

This paper presents details of the RWTH large vocabulary continuous speech recognition system used in the VERBMOBIL spontaneous speech translation system. In particular, we report on methods for accelerating the search and algorithms for fast vocal tract normalization (VTN). We focus both on the improvements in word error rate and how to speed up the recognizer with only minimal loss in recognition accuracy. Implementation details and experimental results are given for the VERBMOBIL German development corpus dev99. The 24.6% word error rate of the baseline system is reduced to 22.8% using VTN. Decreasing the real-time factor by a factor of 5 resulted in only a small degradation in recognition performance of 2% relative on average. Furthermore, we study incremental methods for reducing the response time of the online speech recognizer and an efficient method to reduce the density of word graphs. 1. Introduction This paper describes the RWTH large vocabulary continuous speech recogniti...

Decoders that make use of token-passing restrict their search space by various types of token pruning. With use of the Language Model Look-Ahead (LMLA) technique it is possible to increase the number of tokens that can be pruned without loss of decoding precision. Unfortunately, for token passing decoders that use single static pronunciation prefix trees, full n-gram LMLA increases the needed number of language model probability calculations considerably. In this paper a method for applying full n-gram LMLA in a decoder with a single static pronunciation tree is introduced. The experiments show that this method improves the speed of the decoder without an increase of search errors.