1 research outputs found
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition
Building conversational speech recognition systems for new languages is
constrained by the availability of utterances that capture user-device
interactions. Data collection is both expensive and limited by the speed of
manual transcription. In order to address this, we advocate the use of neural
machine translation as a data augmentation technique for bootstrapping language
models. Machine translation (MT) offers a systematic way of incorporating
collections from mature, resource-rich conversational systems that may be
available for a different language. However, ingesting raw translations from a
general purpose MT system may not be effective owing to the presence of named
entities, intra sentential code-switching and the domain mismatch between the
conversational data being translated and the parallel text used for MT
training. To circumvent this, we explore the following domain adaptation
techniques: (a) sentence embedding based data selection for MT training, (b)
model finetuning, and (c) rescoring and filtering translated hypotheses. Using
Hindi as the experimental testbed, we translate US English utterances to
supplement the transcribed collections. We observe a relative word error rate
reduction of 7.8-15.6%, depending on the bootstrapping phase. Fine grained
analysis reveals that translation particularly aids the interaction scenarios
which are underrepresented in the transcribed data.Comment: Accepted by IEEE ASRU workshop, 201