We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages. We scrape text from multiple genres including blogs, online news, translated TED talks, and subtitles. Using linearly interpolated language models, we find that blogs and movie subtitles are more relevant for language modeling of conversational telephone speech and obtain large reductions in out-of-vocabulary keywords.

We describe the use of text data scraped from the web to augment language models for Automatic Speech Recognition and Keyword Search for Low Resource Languages. We scrape text from multiple genres including blogs, online news, translated TED talks, and subtitles. Using linearly interpolated language models, we find that blogs and movie subtitles are more relevant for language modeling of conversational telephone speech and obtain large reductions in out-of-vocabulary keywords.

We describe a system to collect web data for Low Resource Languages, to augment language model training data for Automatic Speech Recognition (ASR) and keyword search by reducing the Out-of-Vocabulary (OOV) rates–words in the test set that did not appear in the training set for ASR. We test this system on seven Low Resource Languages from the IARPA Babel Program: Paraguayan Guarani, Igbo, Amharic, Halh Mongolian, Javanese, Pashto, and Dholuo.