Question: What model do they use as the feature extractor?
Evidence: Recent advances in Machine Learning (ML) and, in particular, in Deep Neural Networks (DNN) have allowed the development of highly accurate predictive systems for numerous applications. Among others, health has received significant attention due to the potential of ML-based diagnostic, monitoring and therapeutic systems, which are fast (when compared to traditional diagnostic processes), easily distributed and cheap to implement (many such systems can be executed in mobile devices). Furthermore, these systems can incorporate biometric data to perform non-invasive diagnostics.
Among other data types, speech has been proposed as a valuable biomarker for the detection of a myriad of diseases, including: neurological conditions, such as Alzheimer’s BIBREF0, Parkinson’s disease (PD) BIBREF1 and Amyotrophic Lateral Sclerosis BIBREF2; mood disorders, such as depression, anxiety BIBREF3 and bipolar disorder BIBREF4; respiratory diseases, such as obstructive sleep apnea (OSA) BIBREF5. However, temporal and financial constraints, lack of awareness in the medical community, ethical issues and patient-privacy laws make the acquisition of medical data one of the greatest obstacles to the development of health-related speech-based classifiers, particularly for deep learning models. For this reason, most systems rely on knowledge-based (KB) features, carefully designed and selected to model disease symptoms, in combination with simple machine learning models (e.g. Linear classifiers, Support Vector Machines). KB features may not encompass subtler symptoms of the disease, nor be general enough to cover varying levels of severity of the disease. To overcome this limitation, some works have instead focused on speaker representation models, such as Gaussian Supervectors and i-vectors. For instance, Garcia et al. BIBREF1 proposed the use of i-vectors for PD classification and Laaridh et al. BIBREF6 applied the i-vector paradigm to the automatic prediction of several dysarthric speech evaluation metrics like intelligibility, severity, and articulation impairment. The intuition behind the use of these representations is the fact that these algorithms model speaker variability, which should include disease symptoms BIBREF1.
Proposed by Snyder et al., x-vectors are discriminative deep neural network-based speaker embeddings, that have outperformed i-vectors in tasks such as speaker and language recognition BIBREF7, BIBREF8, BIBREF9. Even though it may not be evident that discriminative data representations are suitable for disease detection when trained with general datasets (that do not necessarily include diseased patients), recent works have shown otherwise. X-vectors have been successfully applied to paralinguistic tasks such as emotion recognition BIBREF10, age and gender classification BIBREF11, the detection of obstructive sleep apnea BIBREF12 and as a complement to the detection of Alzheimer's Disease BIBREF0. Following this line of research, in this work we study the hypothesis that speaker characteristics embedded in x-vectors extracted from a single network, trained for speaker identification using general data, contain sufficient information to allow the detection of multiple diseases. Moreover, we aim to assess if this information is kept even when language mismatch is present, as has already been shown to be true for speaker recognition BIBREF8. In particular, we use the x-vector model as a feature extractor, to train Support Vector Machines for the detection of two speech-affecting diseases: Parkinson's disease (PD) and obstructive sleep apnea (OSA).
PD is the second most common neurodegenerative disorder of mid-to-late life after Alzheimer’s disease BIBREF13, affecting 1% of people over the age of 65. Common symptoms include bradykinesia (slowness or difficulty to perform movements), muscular rigidity, rest tremor, as well as postural and gait impairment. 89% of PD patients develop also speech disorders, typically hypokinetic dysarthria, which translates into symptoms such as reduced loudness, monoloudness, monopitch, hypotonicity, breathy and hoarse voice quality, and imprecise articulation BIBREF14BIBREF15.
OSA is a sleep-concerned breathing disorder characterized by a complete stop or decrease of the airflow, despite continued or increased inspiratory efforts BIBREF16. This disorder has a prevalence that ranges from 9% to 38% through different populations BIBREF17, with higher incidence in male and elderly groups. OSA causes mood and personality changes, depression, cognitive impairment, excessive daytime sleepiness, thus reducing the patients' quality of life BIBREF18, BIBREF19. It is also associated with diabetes, hypertension and cardiovascular diseases BIBREF16, BIBREF20. Moreover, undiagnosed sleep apnea can have a serious economic impact, having had an estimated cost of $\$150$ billion in the U.S, in 2015 BIBREF21. Considering the prevalence and serious nature of the two diseases described above, speech-based technology that tests for their existence has the potential to become a key tool for early detection, monitoring and prevention of these conditions BIBREF22.
The remainder of this document is organized as follows. Section SECREF2 presents the background concepts on speaker embeddings, and in particular on x-vectors. Section SECREF3 introduces the experimental setup: the corpora, the tasks, the KB features and the speaker embeddings employed. The results are presented and discussed in section SECREF4. Finally, section SECREF5 summarizes the main conclusions and suggests possible directions for future work.
In particular, we use the x-vector model as a feature extractor, to train Support Vector Machines for the detection of two speech-affecting diseases: Parkinson's disease (PD) and obstructive sleep apnea (OSA).
Answer: The x-vector model.