Paper Info
Title
Detecting levels of interest from spoken dialog with multistream prediction feedback and similarity based hierarchical fusion learning
Abstract
Detecting levels of interest from speakers is a new problem in Spoken Dialog Understanding with significant impact on real world business applications. Previous work has focused on the analysis of traditional acoustic signals and shallow lexical features. In this paper, we present a novel hierarchical fusion learning model that takes feedback from previous multistream predictions of prominent seed samples into account and uses a mean cosine similarity measure to learn rules that improve reclassification. Our method is domain-independent and can be adapted to other speech and language processing areas where domain adaptation is expensive to perform. Incorporating Discriminative Term Frequency and Inverse Document Frequency (D-TFIDF), lexical affect scoring, and low and high level prosodic and acoustic features, our experiments outperform the published results of all systems participating in the 2010 Inter-speech Paralinguistic Affect Subchallenge.
Year
Venue
Keywords
2011
SIGDIAL Conference
inverse document frequency,incorporating discriminative term frequency,previous multistream prediction,spoken dialog understanding,hierarchical fusion learning,inter-speech paralinguistic affect subchallenge,acoustic feature,shallow lexical feature,previous work,detecting level,multistream prediction feedback,traditional acoustic signal,linguistics,information technology,computer science
Field
DocType
Citations 
Spoken dialog,Cosine similarity,Domain adaptation,Computer science,Fusion,Artificial intelligence,Natural language processing,Discriminative model,Paralanguage,tf–idf,Information technology,Speech recognition,Machine learning
Conference
12
PageRank 
References 
Authors
0.64
13
2
Name
Order
Citations
PageRank
William Yang Wang149359.64
Julia Hirschberg22982448.62