Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Conferences, Workshops, Symposiums, and Seminars
  4. Phoneme Recognition using Boosted Binary Features
 
conference paper

Phoneme Recognition using Boosted Binary Features

Roy, Anindya  
•
Magimai.-Doss, Mathew  
•
Marcel, Sébastien  
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE Intl. Conference on Acoustics, Speech and Signal Processing 2011

In this paper, we propose a novel parts-based binary-valued feature for ASR. This feature is extracted using boosted ensembles of simple threshold-based classifiers. Each such classifier looks at a specific pair of time-frequency bins located on the spectro-temporal plane. These features termed as Boosted Binary Features (BBF) are integrated into standard HMM-based system by using multilayer perceptron (MLP) and single layer perceptron (SLP). Preliminary studies on TIMIT phoneme recognition task show that BBF yields similar or better performance compared to MFCC (67.8% accuracy for BBF vs. 66.3% accuracy for MFCC) using MLP, while it yields significantly better performance than MFCC (62.8% accuracy for BBF vs. 45.9% for MFCC) using SLP. This demonstrates the potential of the proposed feature for speech recognition.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

Roy_ICASSP11_2011.pdf

Access type

openaccess

Size

104.05 KB

Format

Adobe PDF

Checksum (MD5)

1859709c903f77ddd4e065b92a1c1b28

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés