A real-time phoneme counting algorithm and application for speech rate monitoring

- The manuscript: “A Real-Time Phoneme Counting Algorithm and Application for Speech Rate Monitoring”.
- Is based on a multidisciplinary research between the school of Electrical Engineering at the Witwatersrand University, Johannesburg, SA, The Medical and Software Engineering Departments at Afeka Academic College of Engineering and the Speech Communication Disorders Department of the Sackler school of Medicine at Tel Aviv University.
- The manuscript describes a novel speech rate monitoring application based on specification of expert speech therapists.
- Although the application stemmed from clinical needs and contains value and interest to Speech disorder therapy, we believe it can also promote speech researchers' interest and therefore more studies in this area, to facilitate speech therapy for persons who need it and improve their daily lives.

Adults who stutter can learn to control and improve their speech fluency by modifying their speaking rate. Existing speech therapy technologies can assist this practice by monitoring speaking rate and providing feedback to the patient, but cannot provide an accurate, quantitative measurement of speaking rate. Moreover, most technologies are too complex and costly to be used for home practice. We developed an algorithm and a smartphone application that monitor a patient's speaking rate in real time and provide user-friendly feedback to both patient and therapist. Our speaking rate computation is performed by a phoneme counting algorithm which implements spectral transition measure extraction to estimate phoneme boundaries. The algorithm is implemented in real time in a mobile application that presents its results in a user-friendly interface. The application incorporates two modes: one provides the patient with visual feedback of his/her speech rate for self-practice and another provides the speech therapist with recordings, speech rate analysis and tools to manage the patient's practice. The algorithm's phoneme counting accuracy was validated on ten healthy subjects who read a paragraph at slow, normal and fast paces, and was compared to manual counting of speech experts. Test-retest and intra-counter reliability were assessed. Preliminary results indicate differences of −4% to 11% between automatic and human phoneme counting. Differences were largest for slow speech. The application can thus provide reliable, user-friendly, real-time feedback for speaking rate control practice.