Discriminative Training of Gender-Dependent Acoustic Models
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Automatic online subtitling of the czech parliament meetings
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Automatic topic identification for large scale language modeling data filtering
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Speaker-clustered acoustic models evaluated on GPU for on-line subtitling of parliament meetings
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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Gender-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.