• Skip to primary navigation
  • Skip to main content
SRI logo
  • About
    • 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
Search
Close
Speech & natural language publications September 1, 2005 Conference Paper

Comparing HMM, Maximum Entropy, and Conditional Random Fields for Disfluency Detection

Citation

Copy to clipboard


Liu, Y., Shriberg, E., Stolcke, A., & Harper, M. P. (2005, September). Comparing HMM, maximum entropy, and conditional random fields for disfluency detection. In Interspeech (pp. 3313-3316).

Abstract

Automatic detection of disfluencies in spoken language is important for making speech recognition output more readable, and for aiding downstream language processing modules. We compare a generative hidden Markov model (HMM)-based approach and two conditional models — a maximum entropy (Maxent) model and a conditional random field (CRF) — for detecting disfluencies in speech. The conditional modeling approaches provide a more principled way to model correlated features. In particular, the CRF approach directly detects the reparandum regions, and thus avoids the use of ad-hoc heuristic rules. We evaluate performance of these three models across two different corpora (conversational speech and broadcast news) and for two types of transcriptions (human transcriptions and recognition output). Overall we find that that the conditional modeling approaches (Maxent and CRF) provide benefit over the HMM approach. Effects of speaking style, word recognition errors, and future directions are also discussed.

↓ Download

Share this

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

Institute

Leadership

Press room

Media inquiries

Compliance

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