This paper pioneers the approach of using crowd work for closed captioning systems. The scenario they target is classes and lectures, where a student can hold up their phone and record the speaker and the sound the transmitted to the crowd workers. The sound that is passed is given as bite sized pieces for the crowd workers to transcribe, and the paper’s implementation of the multiple sequence alignment algorithms takes those transcriptions and combines them. The focus of the tool is very much on real-time captioning so the amount of time a crowd worker can spend on a portion of sound is limited. The authors design interfaces on the worker side to promote continuous transcription, and on the user side to allow them to correct the received transcriptions in real time, enhancing the quality further. The authors had to deal with interesting challenges in resolving errors in the transcription, which they did by a combination of comaparing transcriptions of the same section from different crowd workers, using bigram and trigram data to validate the word ordering. Evaluations showed that precision was stable while coverage increased with increase in the number of workers, while having lower error rate compared to automatic transcription and untrained transcribers.
One thing that needs to be pointed out about this work is that I believe that ASR is always rapidly improving and has made significant strides from when this paper was published. From my own anecdotal experience, Youtube’s automatic closed captions are getting very very close to being fully accurate (however, thinking back on our reading of the Ghost Work book at the beginning of the semester, I wonder if Youtube is cheating a bit and using crowd work intervention for some their videos to help their captioning AI along). I also find that the author’s solution for merging the transcriptions of the different sound bites is interesting. How they would solve that was the first thing that was on my mind because it was not going to be a matter of simply aligning the time stamps because those were definitely going to be imprecise. So I do like their clever multi part solution. Finally, I was a little surprised and disappointed that the WER was at ~45% which was a lot higher than I expected. I was expecting the error rate to be a lot closer to professional transcribers but unfortunately not. The software still has a way to go in that.
- How could you get the error rate down to the professional transcriber’s level? What is going wrong there that is causing it to be that high?
- It’s interesting to me that they couldn’t just play isolated sound clips but instead had to raise and lower volume on a continuous stream for better accuracy. Where are the other places humans work better when they have a continuous stream of data rather than discrete pieces of data?
- Is there an ideal balance between choosing precision and coverage in the context of this paper? This was something that also came up in last week’s readings. Should the user decide what the balance should be? How would they do it when there can be multiple users all at the same location trying to request captioning for the same thing?
