Effects of long-term spectral variability on speaker recognition
Sadaoki Furui · The Journal of the Acoustical Society of America · 1978
One of the most difficult problems in speaker recognition is that the feature parameters frequently vary after a long time interval. We examined this effect on two kinds of speaker recognition; one uses the time pattern of both the fundamental frequency and log-area-ratio parameters and the other uses several kinds of statistical features derived from them. Results of speaker recognition experiments revealed that the long-term variation effects have a great influence on both recognition methods, but are more evident in recognition using statistical parameters. In order to reduce the error rate after a long interval, it is desirable to collect learning samples of each speaker over a long period and measure the weighted distance based on the long-term variability of the feature parameters. When the learning samples are collected over a short period, it is effective to apply spectral equalization using the spectrum averaged over all the voiced portions of the input speech. By this method, an accuracy of 95% can be obtained in speaker verification even after five years using statistical parameters of a spoken word.