- Assaf Michaely
- Justin Scheiner
- Mohammadreza Ghodsi
- Petar Aleksic
- Zelin Wu
It has been shown in the literature that automatic speech recognition systems can greatly benefit from contextual in- formation [ref]. The contextual information can be used to simplify the search and improve recognition accuracy. The types of useful contextual information can include the name of the application the user is in, the contents on the user’s phone screen, user’s location, a certain dialog state, etc. Building a separate language model for each of these types of context is not feasible due to limited resources or limited amount of training data. In this paper we describe an approach for unsupervised learning of contextual information and automatic building of contextual (biasing) models. Our approach can be used to build a large number of small contextual models from a lim- ited amount of available unsupervised training data. We de- scribe how n-grams relevant for a particular context are au- tomatically selected as well as how an optimal size of a final contextual model built is chosen. Our experimental results show great accuracy improvements for several types of con- text.
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