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On Convolutional LSTM Modeling for Joint Wake-Word Detection and Text Dependent Speaker Verification
2018
Interspeech 2018
The task of personalized keyword detection system which also performs text dependent speaker verification (TDSV) has received substantial interest recently. Conventional approaches to this task involve the development of the TDSV and wakeup-word detection systems separately. In this paper, we show that TDSV and keyword spotting (KWS) can be jointly modeled using the convolutional long short term memory (CLSTM) model architecture, where an initial convolutional feature map is further processed
doi:10.21437/interspeech.2018-1759
dblp:conf/interspeech/KumarYG18
fatcat:7b4ycebkufeypmapiowad4vfj4