MGB-3 but system: Low-resource ASR on Egyptian YouTube data
Karel Vesely, Baskar Karthick Murali, Mireia Díez, Karel Beneš · 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · 2017
This paper presents a series of experiments we performed during our work on the MGB-3 evaluations. We both describe the submitted system, as well as the post-evaluation analysis. Our initial BLSTM-HMM system was trained on 250 hours of MGB-2 data (Al-Jazeera), it was adapted with 5 hours of Egyptian data (YouTube). We included such techniques as diarization, n-gram language model adaptation, speed perturbation of the adaptation data, and the use of all 4 ‘correct’ references. The 4 references were either used for supervision with a ‘confusion network’, or we included each sentence 4x with the transcripts from all the annotators. Then, it was also helpful to blend the augmented MGB-3 adaptation data with 15 hours of MGB-2 data. Although we did not rank with our single system among the best teams in the evaluations, we believe that our analysis will be highly interesting not only for the other MGB-3 challenge participants.