Gender-Dependent Acoustic Models Fusion Developed for Automatic Subtitling of Parliament Meetings Broadcasted by the Czech TV

Abstract

AbstractGender-dependent (male/female) acoustic models are more acoustically homogeneous and therefore give better recognition performance than single gender-independent model. This paper deals with a problem how to use these gender-based acoustic models in a real-time LVCSR (Large Vocabulary Continuous Speech Recognition) system that is for more than one year used by the Czech TV for automatic subtitling of Parliament meetings that are broadcasted on the channel ČT24. Frequent changes of speakers and the direct connection of the LVCSR system to the TV audio stream require switching/fusion of models automatically and as soon as possible. The paper presents various techniques based on using the output probabilities for quick selection of a better model or their combinations. The best proposed method achieved over 11% relative WER reduction in comparision with the GI model.KeywordsFusion MethodTotal ProbabilityAcoustic ModelOutput ProbabilityWord Error RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.