Freitag, 13.11.2020, 10.00 Uhr

Investigations on Neural Networks, Discriminative Training Criteria and Error Bounds

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  • Referent: Diplom-Informatiker Markus Nußbaum-Thom



The task of an automatic speech recognition system is to convert speech signals into written text by choosing the recognition result according to a statistical decision rule. The discriminative training of the underlying statistical model is an essential part to improve the word error rate performance of the system. In automatic speech recognition a mismatch exists between the loss used in the word error rate performance measure, the loss of the decision rule and the loss of the discriminative training criterion.

In the course of this thesis the analysis of this mismatch leads to the development of novel error bounds and training criteria. The novel training criteria are evaluated in practical speech recognition experiments.

In summary, we come to the conclusion the statistical model is able to compensate for this mismatch if the discriminative training criterion involves the loss of the performance measure.


Es laden ein: die Dozentinnen und Dozenten der Informatik