Marcin Witkowski

TL;DR: This paper addresses a replay spoofing attack against a speaker recognition system by detecting that the analysed signal has passed through multiple analogue-to-digital conversions by modelling the subband spectrum and using the proposed features derived from the linear prediction analysis.

TL;DR: Statistics of pauses appearing in Polish as a potential source of biometry information for automatic speaker recognition were described and quantity and duration of filled pauses, audible breaths, and correlation between the temporal structure of speech and the syntax structure of the spoken language were the features which characterize speakers most.

TL;DR: The results of experiments performed using simulated and measured room impulse responses for various reverberation time values indicate superior performance of the proposed sparse split Bregman (SSB) method over state-of-the-art non-sparse and sparse MCLP-based dereverberation methods in terms of standard evaluation measures and as pre-processsing to the automatic speech recognition.

TL;DR: A supporting system of voice analysis for emergency call centers is being developed at AGH University of Science and Technology in Krakow to provide an innovative supporting tool for rapid and accurate assessment of caller profile.

TL;DR: The aim of the research was to develop algorithms that would automatically support classification of input sequences into one of the Generalized Use Classes, or GUCs, and to reveal the ambiguity and hesitation of the experts during the manual target size determination process.