Speech processing

Gender-dependent gmm-ubm for tracking parkinson's disease progression from speech

Parkinson’s disease (PD) severity is evaluated by neurologist experts by means of several tests. One of them is the Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS–UPDRS). The main hypothesis is that changes in the speech of …

Non-linear dynamics characterization from wavelet packet transform for automatic recognition of emotional speech

A new set of features based on non-linear dynamics measures obtained from the wavelet packet transform for the automatic recognition of “fear-type” emotions in speech is proposed. The experiments are carried out using three different databases with a …

Parkinson's Disease Progression Assessment from Speech Using GMM-UBM.

Gaussian Mixture Model Universal Background Model (GMM-UBM) approach is used to assess the Parkinson’s disease (PD) progression per speaker. The disease progression is assessed individually per patient following a user modeling-approach. Voiced and …

Towards an automatic monitoring of the neurological state of Parkinson's patients from speech

The suitability of articulation measures and speech intelligibility is evaluated to estimate the neurological state of patients with Parkinson's disease (PD). A set of measures recently introduced to model the articulatory capability of PD patients …

Wavelet-based time-frequency representations for automatic recognition of emotions from speech

The interest in emotion recognition from speech has increased in the last decade. Emotion recognition can improve the quality of services and the quality of life of people. One of the main problems in emotion recognition from speech is to find …

Word accuracy and dynamic time warping to assess intelligibility deficits in patients with parkinsons disease

Parkinson's disease patients develop several impairments related to the speech production process. The deficits of the speech of the patients include reduction in the phonation, articulation, prosody and intelligibility capabilities. Related studies …

Automatic detection of Parkinson's disease from continuous speech recorded in non-controlled noise conditions

Automatic classification of Parkinson's disease (PD) speakers and healthy controls (HC) is performed considering speech recordings collected in non-controlled noise conditions. The speech tasks include six sentences and a read text. The recording is …

Automatic emotion recognition in compressed speech using acoustic and non-linear features

Automatic recognition of emotions in speech has attracted the attention of the research community in recent years. Some of the most relevant proposed applications of it are in call-centers. In these scenarios the speech is distorted by compression …

Emotion recognition from speech under environmental noise conditions using wavelet decomposition

Automatic emotion recognition considering speech signals has attracted the attention of the research community in the last years. One of the main challenges is to find suitable features to represent the affective state of the speaker. In this paper, …

Time dependent ARMA for automatic recognition of fear-type emotions in speech

The speech signals are non-stationary processes with changes in time and frequency. The structure of a speech signal is also affected by the presence of several paralinguistics phenomena such as emotions, pathologies, cognitive impairments, among …