• Skip to primary navigation
  • Skip to main content
SRI InternationalSRI mobile logo

SRI International

SRI International - American Nonprofit Research Institute

  • About
    • Blog
    • Press room
  • Expertise
    • Advanced imaging systems
    • Artificial intelligence
    • Biomedical R&D services
    • Biomedical sciences
    • Computer vision
    • Cyber & formal methods
    • Education and learning
    • Innovation strategy and policy
    • National security
    • Ocean & space
    • Quantum
    • QED-C
    • Robotics, sensors & devices
    • Speech & natural language
    • Video test & measurement
  • Ventures
  • NSIC
  • Careers
  • Contact
  • 日本支社
Show Search
Hide Search
Speech & natural language publications February 1, 2010 Article

Multi-view semi-supervised learning for dialog act segmentation of speech

SRI International February 1, 2010

Citation

Copy to clipboard


U. Guz, S. Cuendet, D. Hakkani-Tür, and G. Tur, “Multi-view semi-supervised learning for dialog  act segmentation of speech,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, pp. 320–329, Feb. 2010.

Abstract

Sentence segmentation of speech aims at determining sentence boundaries in a stream of words as output by the speech recognizer. Typically, statistical methods are used for sentence segmentation. However, they require significant amounts of labeled data, preparation of which is time-consuming, labor-intensive, and expensive. This work investigates the application of multi-view semi-supervised learning algorithms on the sentence boundary classification problem by using lexical and prosodic information. The aim is to find an effective semi-supervised machine learning strategy when only small sets of sentence boundary-labeled data are available. We especially focus on two semi-supervised learning approaches, namely, self-training and co-training. We also compare different example selection strategies for co-training, namely, agreement and disagreement.[…]

↓ Download

↓ Download

Share this

Facebooktwitterlinkedinmail

Publication, Speech & natural language publications Article

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you.

Expect a response within 48 hours.

Career call to action image

Make your own mark.

Search jobs
Our work

Case studies

Publications

Timeline of innovation

Areas of expertise

Blog

Institute

Leadership

Press room

Media inquiries

Compliance

Privacy policy

Careers

Job listings

Contact

SRI Ventures

Our locations

Headquarters

333 Ravenswood Ave
Menlo Park, CA 94025 USA

+1 (650) 859-2000

Subscribe to our newsletter

日本支社

SRI International

  • Contact us
  • Privacy Policy
  • Cookies
  • DMCA
  • Copyright © 2022 SRI International