Name-aware speech recognition for interactive question answering

In this work we show how interactivity in a voice-enabled question answering application may improve speech recognition. We allow the user to provide a target named entity before asking the question. Then we build a named entity specific language model using the documents containing the named entity. The question-specific model is obtained by merging the named entity specific model with the model built on a set of questions. We present a set of experiments using the TREC question set on the AQUAINT corpus. The question-specific language model is compared with the baseline model built by merging a model of the AQUAINT corpus and past TREC questions. The question-specific model achieves 32.2% reduction in word error rate from the baseline using the questions where pronominal references are resolved.