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We tested the influence of fundamental oscillation (fo) on human and machine speaker recognition performance in vocalic test utterances. In experiment I, we trained a Gaussian-Mixture model on 15 speakers (80 multi-word utterances each) and tested it with sustained vowel utterances (/a:/, /i:/ and /u:/) under six fo conditions, three changing (fall, rise, fall-rise) and three steady-state (high, mid, low). Results revealed better performance for the steady-state compared to the changing conditions and within the steady-state condition, performance was poorest for high fo. In experiment II, we tested 9 human listeners on a subset of 4 speakers from experiment I. They went through two training tasks (training 1: multi-word utterances; training 2: words). In the test, they recognized speakers based on the same vocalic utterances as in experiment I (for these 4 speakers). Results showed that performance was about equally high for the changing and steady-state vowels, however, in the steady-state condition performance was best for high fo vowels. The experiments suggest that (a) fo has an influence on the strength of speaker specific characteristics in vowels and (b) humans - compared to machines - pay attention to different acoustic information in vocalic utterances for speaker recognition.

Influences of Fundamental Oscillation on Speaker Identification in Vocalic Utterances by Humans and Computers