Unsupervised Acoustic Model Training for Simultaneous Lecture Translation in Incremental and Batch Mode

In this work the theoretical concepts of unsupervised acoustic model training and the application and evaluation of unsupervised training schemes are described. Experiments aiming at speaker adaptation via unsupervised training are conducted on the KIT lecture translator system. Evaluation takes place with respect to training e ciency and overall system performance in dependency of the available training data. Domain adaptation experiments are conducted on a system trained for European parliament plenary session speeches with help of unsupervised iterative batch training. Major focus is on transcription pre-processing methods and confidence measure based weighting and thresholding on word level for data selection. The objective is to lay the foundation for an unsupervised adaptation framework based on acoustic model training for use in KIT’s simultaneous speech-to-speech lecture translation system. Experimental results show, that it is of advantage to let the Viterbi algorithm during training decide which pronunciations to use and where to insert which noise words, instead of fixating these informations in the transcriptions. With weighting and thresholding it is possible to improve unsupervised training in all test cases. Tests of iterative incremental approaches show that potential performance gains strongly correlate to the performance of the baseline systems. Considerable performance gains are observable after only one iteration of unsupervised batch training with applied transcription pre-processing, weighting and thresholding.