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We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency(More)
We present PEFAC, a fundamental frequency estimation algorithm that is able to identify the pitch of voiced frames reliably even at negative signal to noise ratios. The algorithm combines non-linear amplitude compression, to attenuate narrow-band noise components, with a comb-filter applied in the log-frequency power spectral domain, whose impulse response(More)
We present a novel approach to speaker likability classification. Our algorithm, instead of extracting a large number of features, identifies a small set of features which represent perceptual speech characteristics. For classification, linear support vector machines are used. We train and evaluate the performance on the Interspeech speaker trait challenge(More)
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