We show that high pulse/low pulse, heart rate and skin conductance recognition can reach good accuracies using classification on a large group of 4k audio features extracted from sustained vowels and breathing periods. A database containing audio, heart rate and skin conductance recordings from 19 subjects is established for evaluation of audio-based bio-signal recognition. On this database in speaker-dependent testing, heart rate and skin conductance can be determined with a correlation coefficient of .861/.960 and mean absolute error of 8.1 BPM/88.2 µMhO for regression based on sustained vowels recorded from a room microphone. Using the same set-up, a high pulse/low pulse classification can reach an unweighted accuracy of 82.7%. The results are largely independent from microphone type and the two bio-signals can be determined from breathing periods as well. Performance does, however, degrade in speaker-independent setting.
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