• Skip to primary navigation
  • Skip to main content
SRI logo
  • About
    • Press room
    • Our history
  • 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 January 1, 1996

Noise-resistant Feature Extraction and Model Training for Robust Speech Recognition

Citation

Copy to clipboard


Sankar, A., Stolcke, A., Chung, T., Neumeyer, L., Weintraub, M., Franco, H., & Beaufays, F. (1996, February). Noise-resistant feature extraction and model training for robust speech recognition. In Proceedings of the 1996 DARPA CSR Workshop, Ardenhouse, NY.

Abstract

In this paper we report on our recent work on noise-robust feature extraction and model training to alleviate the mismatch caused by different microphones and ambient room noise in the context of the 1995 DARPA-sponsored H3 benchmark test, which used the unlimited-vocabulary North American Business News (NABN) database. We present a novel noise-robust feature extraction algorithm that is a combination of our previously developed minimum mean square error (MMSE) log-energy estimation algorithm and the probabilistic optimum filtering (POF) algorithm. We also studied an approach based on training the automatic speech recognition (ASR) system with previously collected noisy speech. While both the above approaches gave significant improvements, it was found that combining them gave the best results. We also report on a new part-of-speech (POS) language model that makes it possible to train robust POS language models that incorporate longer contexts than is possible with word-based language models. Preliminary results using this approach were encouraging. 

↓ 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