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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 constrained scenarios with 2-hour data selections given a 1-hour seed set. We expand on this to investigate what happens with larger selections and fewer constraints on language modeling data. Our results, on four languages from the final BABEL OP3 period, show that active learning is helpful at larger selections with consistent gains up to 14 hours. We also find that the impact of additional language model data is orthogonal to the impact of the active learning selection criteria.

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 subset of highly representative data chosen from a large pool of untranscribed audio need be transcribed in order to approach the performance of the system trained with much larger amounts of transcribed audio. In this paper, we compare two basic approaches to active learning: a supervised approach in which we build a speech recognition system from a small amount of seed data in order to make the selection of a limited amount of additional audio for transcription, and an unsupervised approach in which no intermediate system recognition system built from seed data is necessary. Our best unsupervised approach performs quite close to our supervised approach, with both outperforming a random selection scheme.