Abstract
In recent years, applications like Apple’s Siri or Microsoft’s Cortana have created the illusion that one can actually “chat” with a machine. However, a perfectly natural human-machine interaction is far from real as none of these tools can empathize. This issue has raised an increasing interest in speech emotion recognition systems, as the possibility to detect the emotional state of the speaker. This possibility seems relevant to a broad number of domains, ranging from man-machine interfaces to those of diagnostics. With this in mind, in the present work, we explored the possibility of applying a precision approach to the development of a statistical learning algorithm aimed at classifying samples of speech produced by children with developmental disorders(DD) and typically developing(TD) children. Under the assumption that acoustic features of vocal production could not be efficiently used as a direct marker of DD, we propose to apply the Emotional Modulation function(EMF) concept, rather than running analyses on acoustic features per se to identify the different classes. The novel paradigm was applied to the French Child Pathological & Emotional Speech Database obtaining a final accuracy of 0.79, with maximum performance reached in recognizing language impairment (0.92) and autism disorder (0.82).
Highlights
Star Trek fans will remember EMH, the Emergency Medical Holographic program that had the appearance of a reliable, middle aged family doctor
Stemming from this idea, we explored the possibility of applying an emotion-driven approach to the development of a personalized statistical learning algorithm aimed at classifying samples of speeches produced by typically developing children (TD) and by children with autism disorder (AD), specific language impairment (SLI), Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS)
We introduce a novel signal processing paradigm that exploits the individual emotional modulation occurring during speech in order to model atypical behaviors that are symptomatic of DD
Summary
Star Trek fans will remember EMH, the Emergency Medical Holographic program that had the appearance of a reliable, middle aged family doctor. Whereas some recent approaches exploit generative adversarial networks to augment artificially the data space[20], other methods can be drawn from the fast-developing area of ‘personalized medicine’, i.e. the growing knowledge that diagnostic and therapeutic strategies should take variability into account, thence applying highly individualized approaches to patients. This message – that has been largely received in the domain of oncology21 – is becoming increasingly more relevant to studies in other areas, including neuropsychiatry (see for instance[22,23,24])
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
