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A significant part of information carried in speech signal refers to the speaker. This paper deals with investigating alcohol intoxication based on analyzing recorded speech signal. Speech changes resulting from alcohol intoxication were investigated in the waveform of glottal pulses estimated from speech by applying the Iterative Adaptive Inverse Filtering (IAIF). Experimental results show that analysis of glottal excitation appears to be a useful approach to provide evidence of alcohol intoxication of over 1‰. At this alcohol level, the associated negative events influence professional performance and may involve fatal accidents in some cases. Via analyzing the speech signal, the speaker could be automatically monitored without their active cooperation. For use in our experiments, a new collection of Czech alcoholized speech consisting of phonetically identical speech data spoken in both sober and intoxicated state was created.

This paper describes an approach for enhancing the robustness of isolated words recognizer by extending its flexibility in the domain of speaker's variable vocal effort level. An analysis of spectral properties of spoken vowels in four various speaking modes (whispering, soft, normal, and loud) confirm consistent spectral tilt changes. Severe impact of vocal effort variability on the accuracy of a speakerdependent word recognizer is presented and an efficient remedial measure using multiple-model framework paired with accurate speech mode detector is proposed.

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