
Filtering of Filter‐Bank Energies for Robust Speech Recognition
Author(s) -
Jung HoYoung
Publication year - 2004
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.04.0203.0033
Subject(s) - cepstrum , decorrelation , speech recognition , robustness (evolution) , mel frequency cepstrum , computer science , filter bank , hidden markov model , word error rate , filter (signal processing) , pattern recognition (psychology) , feature (linguistics) , artificial intelligence , feature extraction , algorithm , computer vision , biochemistry , chemistry , linguistics , philosophy , gene
We propose a novel feature processing technique which can provide a cepstral liftering effect in the log‐spectral domain. Cepstral liftering aims at the equalization of variance of cepstral coefficients for the distance‐based speech recognizer, and as a result, provides the robustness for additive noise and speaker variability. However, in the popular hidden Markov model based framework, cepstral liftering has no effect in recognition performance. We derive a filtering method in log‐spectral domain corresponding to the cepstral liftering. The proposed method performs a high‐pass filtering based on the decorrelation of filter‐bank energies. We show that in noisy speech recognition, the proposed method reduces the error rate by 52.7% to conventional feature.