Analysing Speech for Clinical Applications

摘要

The boost in speech technologies that we have witnessed over the last decade has allowed us to go from a state of the art in which correctly recognizing strings of words was a major target, to a state in which we aim much beyond words. We aim at extracting meaning, but we also aim at extracting all possible cues that are conveyed by the speech signal. In fact, we can estimate bio-relevant traits such as height, weight, gender, age, physical and mental health. We can also estimate language, accent, emotional and personality traits, and even environmental cues. This wealth of information, that one can now extract with recent advances in machine learning, has motivated an exponentially growing number of speech-based applications that go much beyond the transcription of what a speaker says. In particular, it has motivated many health related applications, namely aiming at non-invasive diagnosis and monitorization of diseases that affect speech.