An investigation into front-end signal processing for speaker normalization

S. Umesh, Rohit Sinha, Bharath Kumar Sriperumbudur

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Our investigation into the front-end signal processing for maximum likelihood based speaker normalization reveals that in the linear scaling model, it is more appropriate (and evidently more correct) to assume that the spectral envelopes of any two speakers for same sound are linearly scaled versions of one and another, rather than assuming that the whole magnitude spectra (including pitch harmonics) are scaled. The use of the proposed model and its implementation results in about 4% and 7% relative improvement for adults and children respectively on a digit recognition task.

Original languageEnglish (US)
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
Publication statusPublished - 2004

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this