Year: 2007
Type: book-chapter
Abstract: In this chapter, we proposed a novel approach that improves the robustness of prosody dependent language modeling by leveraging the dependence between prosody and syntax. In our experiments on Radio News Corpus, a factorial prosody dependent language model estimated using our proposed approach has achieved as much as 31% reduction of the joint perplexity over a prosody dependent language model estimated using the standard Maximum Likelihood approach. In recognition experiments, our approach results in a 1% improvement in word recognition accuracy, 0.7% improvement in accent recognition accuracy and 1.5% improvement in intonational phrase boundary (IPB) recognition accuracy over the baseline prosody dependent recognizer. The study in the chapter shows that
DOI: 10.5772/4757
Authors: Ken Chen, Mark A., Jennifer S.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W1568653222