Language modeling for voice search: A machine translation approach

This paper presents a novel approach to language modeling for voice search based on the idea and method of statistical machine translation. We propose an n-gram based translation model that can be used for listing-to-query translation. We then leverage the query forms translated from listings to improve language modeling. The translation model is trained in an unsupervised manner using a set of transcribed voice search queries. Experiments show that the translation approach yielded drastic perplexity reductions compared with a baseline language model where no translation is applied.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Xiao Li, Yun-Cheng Ju, Geoffrey Zweig, Alex Acero
• Keywords: ICASSP 2008, Language Modeling, N-gram Based Translation, Signal Processing, Translation Model