The advantage to training speech recognition with machine learning is that despite the potentially high number of training examples, not all possible human utterances need to be learned. The artificial intelligence behind services like Microsoft LUIS allows it to correctly recognize even similar utterances or utterances with spelling errors. Various highly complex and computationally intensive algorithms work together for this purpose. For example, the NLU component requires a lexicon, a parser and grammar rules of the corresponding language. Predefined ontologies - or concept networks - are used for this purpose. This enables language processing to draw conclusions, for example, as to whether terms have a similar meaning. At arvato Systems, for example, we were able to recognize the intent "send email" from the utterances "send email," "forward email," and "send email" during the development of a service chatbot - without having to explicitly train all these verbs. Although the accuracy with which the NLU was convinced that the analyzed utterance matched the intent decreased, the algorithm was still more than 70 percent confident of detecting the correct intent. This shows that the machine learning model works flexibly enough to cope with deviations in communication.