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Analysing acoustic model changes for active learning in automatic speech recognition
2017
2017 International Conference on Systems, Signals and Image Processing (IWSSIP)
In active learning for Automatic Speech Recognition (ASR), a portion of data is automatically selected for manual transcription. The objective is to improve ASR performance with retrained acoustic models. The standard approaches are based on confidence of individual sentences. In this study, we look into an alternative view on transcript label quality, in which Gaussian Supervector Distance (GSD) is used as a criterion for data selection. GSD is a metric which quantifies how the model was
doi:10.1109/iwssip.2017.7965609
dblp:conf/iwssip/WuNSH17
fatcat:sybspv53y5amxge7e7rvk7ll74