The paper provides an analysis of automatic speech recognition
systems (ASR) based on multilingual BLSTM, where we used multi-task
training with separate classification layer for each language. The
focus is on low resource languages, where only a limited
amount of transcribed speech is available. In such
scenario, we found it
essential to train the ASR systems in a multilingual fashion and we
report superior results
obtained with pre-trained multilingual BLSTM on this task.
The high resource languages are also
taken into account and we show the importance of language richness
for multilingual training. Next, we present the performance of this
technique as a function of amount of target language data.
The importance of including context information into BLSTM multilingual systems is also stressed, and we report increased resilience of large NNs to overtraining in case of multi-task
training.