On-the-fly user modeling for cost-sensitive correction of speech transcripts

We propose an on-the-fly updating framework for cost-sensitive manual correction of automatically recognized speech transcripts. This framework trains cost-models during the transcription process, and does not require the transcriber enrollment necessary in previous work. We use a baseline method that optimizes a segmentation into segments to supervise or not to supervise in a cost-sensitive fashion that minimizes human effort, and introduce a much faster algorithm for computing such a segmentation that can be used for on-the-fly updates. Besides removing the need to carry out enrollments, experiments show that our updating framework results in 28% higher human supervision efficiency than previous cost-sensitive approaches.