Minimizing annotation effort for adaptation of speech-activity detection systems

Annotating audio data for the presence and location of speech is a time-consuming and therefore costly task. This is mostly because annotation precision greatly affects the performance of the speech-activity detection (SAD) systems trained with this data, which means that the annotation process must...

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Guardado en:
Detalles Bibliográficos
Publicado: 2016
Materias:
Active learning
Adaptation
Annotation
Speech-activity detection
Artificial intelligence
Budget control
Speech
Speech communication
Speech processing
Active Learning
Audio samples
Baseline systems
Simple approach
Speech activity detections
Training data
Speech recognition
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2308457X_v08-12-September-2016_n_p3002_Ferrer
http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p3002_Ferrer
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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