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- W2516403777 abstract "This paper deals with the task of Speech Activity Detection (SAD). Our goal is to develop a SAD modulesuitable for a system for broadcast data transcription. Various Deep Neural Networks (DNNs) are evaluatedfor this purpose. Training of DNNs is performed using speech and non-speech data as well as artificial datacreated by mixing of both these data types at a desired level of Signal-to-Noise Ratio (SNR). The outputfrom each DNN is smoothed using a decoder based on Weighted Finite State Transducers (WFSTs). Thepresented experimental results show that the use of the resulting SAD module leads to a) a slight improvementin transcription accuracy and b) a significant reduction in the computation time needed for transcription." @default.
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- W2516403777 date "2016-01-01" @default.
- W2516403777 modified "2023-09-26" @default.
- W2516403777 title "Study on the Use of Deep Neural Networks for Speech Activity Detection in Broadcast Recordings" @default.
- W2516403777 doi "https://doi.org/10.5220/0005952700450051" @default.
- W2516403777 hasPublicationYear "2016" @default.
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