Using Acoustic Deep Neural Network Embeddings to Detect Multiple Sclerosis from Speech

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. It affects cognitive and motor functions, and the limitation of executive functions can also manifest itself in speech production. Due to this, automatic speech analysis might serve as an effective technique for assessing MS, or for monitoring the status of the patient. However, choosing the features to be extracted from the recordings is not straightforward. In the past few years, general feature extractors such as i-vectors, d-vectors and x-vectors have found their way into automatic speech analysis. In this study we show that there is no need to employ a special neural network architecture such as x-vectors to calculate effective features, but (even more) indicative features can be derived on the basis of a standard Deep Neural Network acoustic model. From our results, these features could effectively be used to distinguish MS subjects from healthy controls, as we measured AUC scores up to 0.935. We found that classification performance depended only slightly on the choice of the hid-den layer used to extract our features, but the speech task per-formed by the subject turned out to be an important factor.