Paper Info
Title
Zero Resource Spoken Audio Corpus Analysis
Abstract
Zero-resource speech processing involves the automatic analysis of a collection of speech data in a completely unsupervised fashion without the benefit of any transcriptions or annotations of the data. In this paper, our zero-resource system seeks to automatically discover important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic time warping (S-DTW) algorithm for acoustic pattern discovery in conjunction with a probabilistic model which treats the topic and pseudo-word identity of each discovered pattern as hidden variables. By applying an Expectation-Maximization (EM) algorithm, our system estimates the latent probability distributions over the pseudo-words and topics associated with the discovered patterns. Using this information, we produce acoustic summaries of the dominant topical themes of the audio document collection.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639335
2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Zero-resource speech processing, spoken term discovery, speech summarization
Speech corpus,Speech processing,Speech coding,Dynamic time warping,Computer science,Natural language processing,Artificial intelligence,Audio signal processing,Speech analytics,Pattern recognition,Audio mining,Speech recognition,Acoustic model
Conference
ISSN
Citations 
PageRank 
1520-6149
11
0.61
References 
Authors
13
3
Name
Order
Citations
PageRank
David F. Harwath1638.34
Timothy J. Hazen288081.55
James Glass33123413.63