On the Mutual Information between Source and Filter Contributions for Voice Pathology Detection
This work addresses voice disorder detection for medical diagnostics, but it is incremental as it builds on existing feature analysis methods without introducing a new paradigm.
The paper tackled automatic voice pathology detection from speech by proposing three feature sets based on speech, glottal signals, and prosody, and assessed their relevance using mutual information measures to interpret discrimination power and redundancy.
This paper addresses the problem of automatic detection of voice pathologies directly from the speech signal. For this, we investigate the use of the glottal source estimation as a means to detect voice disorders. Three sets of features are proposed, depending on whether they are related to the speech or the glottal signal, or to prosody. The relevancy of these features is assessed through mutual information-based measures. This allows an intuitive interpretation in terms of discrimation power and redundancy between the features, independently of any subsequent classifier. It is discussed which characteristics are interestingly informative or complementary for detecting voice pathologies.