Automatic speaker identi?cation typically relies on sophisticated statistical modeling and classi?cation which requires large amounts of data for good performance. However, in actual audio forensics casework, frequently only a few seconds of speech material are available. In this paper, we favor diversity in feature extraction, simple modeling and classi?cation, and constructive combination of congruent classi?cation scores. We use phase, spectral magnitude and F0-related features in speaker identi?cation experiments on a database of 35 speakers most of whom are twins. Using only 4.4 sec. of vowel-like sounds per speaker, we characterize the performance that is reached with individual features and we characterize simple and yet effective ways of classi?cation score fusion. Insights for further research are also presented.
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