The use of random spliced speech for the recognition of familiar voices

Abstract

Various techniques for experimental speech signal manipulation to test recognition of familiar voices have been used, but random spliced speech (RSS) has not been tried. This procedure, which consists of randomly recombining digitally segmented speech signals, offers the advantage of more natural-sounding speech and decreased risk of creating artifacts interfering with perception. Although intelligibility is also destroyed by this technique, RSS preserves the spectral cues related to voice quality, as well as F0 level and range. In the present experiment, various 3.5 s RSS samples of 11 males were presented to 34 listeners, each familiar with only one of the voices. In a recognition task with forced yes/no responses, the index of correct identification was well above chance. An analysis of misindentifications revealed that proximity of F0 means led to more errors than did similarity of long term average spectra. Since speakers with F0 mean less than 2.8 semitones apart were frequently confused, it seems that in such an experimental situation the listeners may be forced to rely on isolated cues, such as F0; this may explain the high number of false identifications, even though the number of false rejections is practically nonexistent.