Abstract:
Following recent evidence that FM features extracted from a sub-band
decomposition of speech are highly uncorrelated, this paper investigates the effect of the
number of auditory scale sub-bands in FM based front-end processing. For this study, a
newly developed robust FM extraction method based on the least square differential ratio is
used to extract features, comprising one FM component per sub-band. Automatic speaker
recognition experiments were conducted on the cellular NIST 2001 database, with the
number of filters in the front-end varied from 6 to 26. Performance degradation was
observed for very low numbers of filters and very high numbers of filters. Results show that
for a 4 kHz speech bandwidth, a minimum of 10 and a maximum of 18 sub-bands is a
suitable choice for speech front-end applications such as automatic speaker recognition.
