Description:
Speech signals contain paralinguistic information with specific cues about a given speaker including the
presence of diseases that may alter their communication capabilities. The automatic classification of
paralinguistic aspects has many potential applications, and has received a good deal of attention by the
research community [1-3]. In particular researchers look at clinical observations in the speech of patients
and try to objectively and automatically measure two main aspects of a given disease: (1) the presence of
a disease via classification of healthy control (HC) subjects and patients, and (2) the level of degradation
of the speech of patients according to a specific clinical scale [4]. These aspects are evaluated using
computer aided methods supported in signal processing and pattern recognition methods.
At the center of these computer aided methods and something that has been developed over the years to
continually improve the diagnosis and the assessment of severity of different pathological diseases is the
particular feature set and extraction method used [5-7]. Many recent studies focused on extracting
features for assessment of pathological speech rely on deep learning strategies [3].
In this project we consider one such approach that uses a parallel representation learning strategy to
model speech signals from patients with different speech disorders [8]. The model uses two types of
autoencoders, a convolutional autoencoder (CAE) and recurrent autoencoder (RAE). Both take as input
a spectrogram and output features derived from a hidden representation in the bottleneck space (i.e. a
compressed representation of the input). In addition, the reconstruction error of the autoencoder in
different spectral components of the speech signal is considered as a feature set.
The aim of this project is to evaluate the performance of the parallel representation learning strategy
using different parameterized representations of the spectrogram (e.g. comparing broadband and
narrowband spectral representations) as well as a wavelet representation to quantify the information
loss for each representation, and the benefit of using all of them together as a multiple input channel.
Methods for quantification include the overall ability of the proposed model to classify different
pathologies and the associated level of degradation of a given patient’s speech, and also comparing the
input and reconstructed speech signals using contours of phonological posteriors [9]. The aim is to
evaluate which group of phonemes are more affected due to the compression of the autoencoders using
the different spectral resolutions and their combinations.
References:
[1] Schuller, B., Batliner, A., 2013. Computational Paralinguistics: Emotion, Affect and Personality in
Speech and Language Processing. John Wiley & Sons.
[2] Schuller, B., et al., 2019. Affective and Behavioural Computing: Lessons Learnt from the First
Computational Paralinguistics Challenge. Computer Speech & Language 53, 156–180.
[3] Cummins, N., Baird, A., Schuller, B., 2018. Speech Analysis for Health: Current State-of-the-Art
and the Increasing Impact of Deep Learning Methods.
[4] Orozco-Arroyave, J.R., et al., 2015. Characterization Methods for the Detection of Multiple Voice
Disorders: Neurological, Functional, and Laryngeal Diseases. IEEE Journal of Biomedical and Health
Informatics 19, 1820–1828.
[5] Dimauro, G., Di-Nicola, V., et al., 2017. Assessment of Speech Intelligibility in Parkinson’s
Disease Using a Speech-to-Text System. IEEE Access 5, 22199–22208.
[6] Orozco-Arroyave, J.R., Vasquez-Correa, J.C., et al., 2016. Towards an Automatic Monitoring of
the Neurological State of the Parkinson’s Patients from Speech, in: IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), pp. 6490–6494.
[7] Schuller, B., Steidl, S., Batliner, A., Hantke, S., Hönig, F., Orozco-Arroyave, J.R., Nöth, E., Zhang,
Y., Weninger, F., 2015. The INTERSPEECH 2015 Computational Paralinguistics Challenge:
Nativeness, Parkinson’s & Eating Condition, in: Proceedings of INTERSPEECH, pp. 478–482.
[8] Vásquez-Correa, Juan Camilo et al. “Parallel Representation Learning for the Classification of
Pathological Speech: Studies on Parkinson’s Disease and Cleft Lip and Palate” Under Review (2020).
[9] Vásquez-Correa, Juan Camilo et al. “Phonet: A Tool Based on Gated Recurrent Neural Networks to
Extract Phonological Posteriors from Speech.”, in: INTERSPEECH (2019).