SAMI: an M-Health application to telemonitor intelligibility and speech disorder severity in head and neck cancers.

Perceptual measures, such as intelligibility and speech disorder severity, are widely used in the clinical assessment of speech disorders in patients treated for oral or oropharyngeal cancer. Despite their widespread usage, these measures are known to be subjective and hard to reproduce. Therefore, an M-Health assessment based on an automatic prediction has been seen as a more robust and reliable alternative. Despite recent progress, these automatic approaches still remain somewhat theoretical, and a need to implement them in real clinical practice rises. Hence, in the present work we introduce SAMI, a clinical mobile application used to predict speech intelligibility and disorder severity as well as to monitor patient progress on these measures over time. The first part of this work illustrates the design and development of the systems supported by SAMI. Here, we show how deep neural speaker embeddings are used to automatically regress speech disorder measurements (intelligibility and severity), as well as the training and validation of the system on a French corpus of head and neck cancer. Furthermore, we also test our model on a secondary corpus recorded in real clinical conditions. The second part details the results obtained from the deployment of our system in a real clinical environment, over the course of several weeks. In this section, the results obtained with SAMI are compared to an a posteriori perceptual evaluation, conducted by a set of experts on the new recorded data. The comparison suggests a high correlation and a low error between the perceptual and automatic evaluations, validating the clinical usage of the proposed application.

Performance analysis of various fundamental frequency estimation algorithms in the context of pathological speech.

Reliable fundamental frequency (f0) extraction algorithms are crucial in many fields of speech research. The current bulk of studies testing the robustness of different algorithms have focused on healthy speech and/or measurements of sustained vowels. Few studies have tested f0 estimations in the context of pathological speech, and even fewer on continuous speech. The present study evaluated 12 available pitch detection algorithms on a corpus of read speech by 24 speakers (8 healthy speakers, 8 speakers with Parkinson's disease, and 8 with head and neck cancer). Two fusion methods' algorithms have been tested: one based on the median of algorithms and one based on the fusion between the best algorithm for voicing detection and the algorithm that generates the most accurate f0 estimations on voiced parts. Our results show that time-domain algorithms, like REAPER, are best for voicing detection while deep neural network algorithms, like FCN- f0, yield better accuracy for the f0 values on voiced parts. The combination of REAPER and FCN- f0 yields the best ratio performance/implementation complexity, since it generates less than 4% errors on voicing detection and less than 5% of gross errors in the estimation of the f0 values for all speaker groups.