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The article focuses on evaluating whether remote, automatic monitoring of cough, a cardinal symptom of tuberculosis (TB), could serve as a noninvasive and low-cost biomarker for monitoring treatment response in patients with TB.

For successful deployment, a South African English speech recognition system must be capable of processing the prevalent accents in this variety of English. Previous work dealing with the different accents of South African English has considered the case in which the accent of the input speech is known. Here we focus on the practical scenario in which the accent of the input speech is unknown and accent identification must occur at recognition time. By means of a set of contrastive experiments, we determine the effect which errors in the identification of the accent have on speech recognition performance. We focus on the specific configuration in which a set of accent-specific speech recognisers operate in parallel, thereby delivering both a recognition hypothesis as well as an identified accent in a single step. We find that, despite their considerable number, the accent identification errors do not lead to degraded speech recognition performance. We conclude that, for our South African English data, there is no benefit of including a more complex explicit accent identification component in the overall speech recognition system. [ABSTRACT FROM AUTHOR]

Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performing cough detection using deep architectures. As most previous studies have adopted features designed for speech recognition, we assess the suitability of these techniques as well as their respective extraction parameters. Short-time Fourier transform (STFT), mel-frequency cepstral coefficients (MFCC) and mel-scaled filter banks (MFB) were evaluated using deep neural networks, convolutional neural networks and long-short term models. We find experimentally that, by regarding each cough sound as a single input feature instead of multiple shorter features, better performance can be achieved. Longer analysis windows also provide enhancement in contrast to the classic 25 ms frame. Although MFCC performance is improved by sinusoidal liftering, STFT and MFB lead to better results. Using MFB and the optimum segment and frame lengths, an improvement exceeding 7% in the area under the receiver operating characteristic curve across all classifiers is achieved.