Automatic parameter tuning and extended training material: Recent advances in the Fraunhofer speech recognition system
Building the acoustic and language models on a larger amount of training data is a well-known method for robustifying automatic speech recognition approaches. The adaption of the decoder settings afterwards, however, is often only marginally addressed (e.g. being manually set or using default values provided by a toolkit). Without proper adaption, these settings are most often sub-optimal and lead to degraded performance without unlocking the full potential of the speech recognizer. Ideally, the decoder settings should be optimized after each modification of the language model and/or the acoustic model of the speech recognition system, a task that is typically too tedious for manual work. In this paper, we employ an automatic optimization technique on the Fraunhofer IAIS speech recognition setup as a subsequent step to training data increase. We will present the improvements of the expanded training data for the acoustic models and the optimization of the decoder settings on the German Difficult Speech Corpus.