3 accepted papers
Data Valuation in machine learning is concerned with quantifying the relative contribution of a training example to a model’s performance. Quantifying the importance of training examples is useful for identifying high and low quality data to curate training datasets and for address data quality issu…
Active learning aims to reduce the time and cost of developing speech recognition systems by selecting for transcription highly informative subsets from large pools of audio data. Previous evaluations at OpenKWS and IARPA BABEL have investigated data selection for low-resource languages in very cons…
Automatic speech recognition (ASR) systems rely on large quantities of transcribed acoustic data. The collection of audio data is relatively cheap, whereas the transcription of that data is relatively expensive. Thus there is an interest in the ASR community in active learning, in which only a small…