Title :
Language model adaptation using auto-induced semantic structures in a voice search system
Author :
Yali Li ; Li, Yali ; Yan, Yonghong
Author_Institution :
ThinkIT Lab., Chinese Acad. of Sci., Beijing, China
Abstract :
In this paper, we study how to generate in-domain data for statistical language model adaptation in a Chinese voice search dialogue system. Given limited amount of in-domain data, we use unsupervised clustering to induce semantic classes and structures from the first part of test data. These structures are further augmented with domain information to generate large amount of in-domain data. Lastly we test on the second part of test data and get a improvement of speech recognition for 6.2%.
Keywords :
natural language processing; pattern clustering; speech recognition; Chinese voice search dialogue system; auto induced semantic structure; in-domain data generation; speech recognition; statistical language model adaptation; unsupervised clustering; voice search system; Acoustics; Adaptation model; Automatic speech recognition; Databases; Hidden Markov models; Laboratories; Natural languages; Speech recognition; Speech synthesis; Testing; semantic class induction; statistic language model adaptation;
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
DOI :
10.1109/ICICISYS.2009.5358161
