# Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition

arXiv: https://arxiv.org/abs/1912.00958 · HTML: https://mlsanity.com/paper/1912.00958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation](https://mlsanity.com/paper/2110.10261/) (2021)

## Abstract (arXiv)

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.