Juan Rafael Orozco-Arroyave

Automatic detection of Parkinson's disease in running speech spoken in three different languages.

The aim of this study is the analysis of continuous speech signals of people with Parkinsons disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.

Characterization Methods for the Detection of Multiple Voice Disorders: Neurological, Functional, and Laryngeal Diseases

This paper evaluates the accuracy of different characterization methods for the automatic detection of multiple speech disorders. The speech impairments considered include dysphonia in people with Parkinsons disease (PD), dysphonia diagnosed in patients with different laryngeal pathologies (LP), and hypernasality in children with cleft lip and palate (CLP). Four different methods are applied to analyze the voice signals including noise content measures, spectral-cepstral modeling, nonlinear features, and measurements to quantify the stability of the fundamental frequency. These measures are tested in six databases: three with recordings of PD patients, two with patients with LP, and one with children with CLP. The abnormal vibration of the vocal folds observed in PD patients and in people with LP is modeled using the stability measures with accuracies ranging from 81% to 99% depending on the pathology. The spectral-cepstral features are used in this paper to model the voice spectrum with special emphasis around the first two formants. These measures exhibit accuracies ranging from 95% to 99% in the automatic detection of hypernasal voices, which confirms the presence of changes in the speech spectrum due to hypernasality. Noise measures suitably discriminate between dysphonic and healthy voices in both databases with speakers suffering from LP. The results obtained in this study suggest that it is not suitable to use every kind of features to model all of the voice pathologies; conversely, it is necessary to study the physiology of each impairment to choose the most appropriate set of features.

Analysis of Speech from People with Parkinson’s Disease through Nonlinear Dynamics

Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet.

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 Parkinsons disease (PD). A set of measures recently introduced to model the articulatory capability of PD patients is considered. Additionally, the speech intelligibility in terms of the word accuracy obtained from the Google® speech recognizer is included. Recordings of patients in three different languages are considered: Spanish, German, and Czech. Additionally, the proposed approach is tested on data recently used in the INTERSPEECH 2015 Computational Paralinguistics Challenge. According to the results, it is possible to estimate the neurological state of PD patients from speech with a Spearmans correlation of up to 0.72 with respect to the evaluations performed by neurologist experts.

Nonlinear dynamics characterization of emotional speech

This paper proposes the application of complexity measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon entropy, Lempel-Ziv complexity and Hurst exponent are extracted from the samples of three databases of emotional speech. Then, statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Polish emotional speech database, on the Berlin emotional speech database and on the LCD emotional database for a three-class problem (neutral, fear and anger emotional states). A procedure for feature selection is proposed based on an affinity analysis of the features. This feature selection procedure is accomplished to select a reduced number of features over the Polish emotional database. Finally, the selected features are evaluated in the Berlin emotional speech database and in the LDC emotional database using a neural network classifier in order to assess the usefulness of the selected features. Global success rates of 72.28%, 75.4% and 80.75%, were obtained for the Polish emotional speech database, the Berlin emotional speech database and the LDC emotional speech database respectively.

Perceptual Analysis of Speech Signals from People with Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient’s speech.

Multi-view representation learning via gcca for multimodal analysis of Parkinson's disease

Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinsons disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a representation of features extracted from handwriting and gait that can be used as a complement to speech-based features. Three different problems are addressed: classification of PD patients vs. healthy controls, prediction of the neurological state of PD patients according to the UPDRS score, and the prediction of a modified version of the Frenchay dysarthria assessment (m-FDA). According to the results, the proposed approach is suitable to improve the results in the addressed problems, specially in the prediction of the UPDRS, and m-FDA scores.

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

The 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 modelingapproach. Voiced and unvoiced segments are extracted and grouped separately to train the models. Additionally, the Bhattacharyya distance is used to estimate the difference between the UBM and the user model. Speech recordings from 62 PD patients (34 male and 28 female) were captured from 2012 to 2015 in four recording sessions. The validation of the models is performed with recordings of 7 patients. All of the patients were diagnosed by a neurologist expert according to the MDSUPDRS-III scale. The features used to model the speech of the patients are validated by doing a regression based on a Support Vector Regressor (SVR). According to the results, it is possible to track the disease progression with a Pearson’s correlation of up to 0.60 with respect to the MDS-UPDRS-III labels.

Spectral and cepstral analyses for Parkinson's disease detection in Spanish vowels and words

About 1% of people older than 65years suffer from Parkinsons disease PD and 90% of them develop several speech impairments, affecting phonation, articulation, prosody and fluency. Computer-aided tools for the automatic evaluation of speech can provide useful information to the medical experts to perform a more accurate and objective diagnosis and monitoring of PD patients and can help also to evaluate the correctness and progress of their therapy. Although there are several studies that consider spectral and cepstral information to perform automatic classification of speech of people with PD, so far it is not known which is the most discriminative, spectral or cepstral analysis. In this paper, the discriminant capability of six sets of spectral and cepstral coefficients is evaluated, considering speech recordings of the five Spanish vowels and a total of 24 isolated words. According to the results, linear predictive cepstral coefficients are the most robust and exhibit values of the area under the receiver operating characteristic curve above 0.85 in 6 of the 24 words.

Evaluation of wavelet measures on automatic detection of emotion in noisy and telephony speech signals

Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been applied is in situations where the human integrity and security are at risk. In this paper we are propossing a set of features based on the Teager energy operator, and several entropy measures obtained from the decomposition signals from discrete wavelet transform to characterize different types of negative emotions such as anger, anxiety, disgust, and desperation. The features are measured in three different conditions: (1) the original speech signals, (2) the signals that are contaminated with noise, or are affected by the presence of a phone channel, and (3) the signals that are obtained after processing using an algorithm for Speech Enhancement based on Karhunen-Love Transform. According to the results, when the speech enhancement is applied, the detection of emotion in speech is increased in up to 22% compared to results obtained when the speech signal is highly contaminated with noise.