1 research outputs found
Pathological speech detection using x-vector embeddings
The potential of speech as a non-invasive biomarker to assess a speaker's
health has been repeatedly supported by the results of multiple works, for both
physical and psychological conditions. Traditional systems for speech-based
disease classification have focused on carefully designed knowledge-based
features. However, these features may not represent the disease's full
symptomatology, and may even overlook its more subtle manifestations. This has
prompted researchers to move in the direction of general speaker
representations that inherently model symptoms, such as Gaussian Supervectors,
i-vectors and, x-vectors. In this work, we focus on the latter, to assess their
applicability as a general feature extraction method to the detection of
Parkinson's disease (PD) and obstructive sleep apnea (OSA). We test our
approach against knowledge-based features and i-vectors, and report results for
two European Portuguese corpora, for OSA and PD, as well as for an additional
Spanish corpus for PD. Both x-vector and i-vector models were trained with an
out-of-domain European Portuguese corpus. Our results show that x-vectors are
able to perform better than knowledge-based features in same-language corpora.
Moreover, while x-vectors performed similarly to i-vectors in matched
conditions, they significantly outperform them when domain-mismatch occurs.Comment: Rejected for publication by peer revie