Florian Hönig

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.

“Looks do matter”—visual attentional biases in adolescent girls with eating disorders viewing body images

Visual attention allocation of adolescent girls with and without an eating disorder while viewing body images of underweight, normal-weight and overweight women was studied using eye tracking. While all girls attended more to specific body parts (e.g. hips, upper legs), eating-disordered girls showed an attentional bias towards unclothed body parts.

Writer identification and verification using GMM supervectors

This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.

The HUMAINE Database

The HUMAINE Database is grounded in HUMAINE’s core emphasis on considering emotion in a broad sense – ‘pervasive emotion’ – and engaging with the way it colours action and interaction. The aim of the database is to provide a resource to which the community can go to see and hear the forms that emotion takes in everyday action and interaction, and to look at the tools that might be relevant to describing it. Earlier chapters in this handbook describe the techniques and models underpinning the collection and labelling of such data. This chapter focuses on conveying the range of forms that emotion takes in the database, the ways that they can be labelled and the issues that the data raises. The HUMAINE Database provides naturalistic clips which record that kind of material, in multiple modalities, and labelling techniques that are suited to describing it. It was clear when the HUMAINE project began that work on databases should form part of it. However there were very different directions that the work might have taken. They were encapsulated early on in the contrast between ‘supportive’ and ‘provocative’ approaches, introduced in an earlier chapter in this handbook. The supportive option was to assemble a body of data whose size and structure allowed it to be used directly to build systems for recognition and/or synthesis. The provocative option was to assemble a body of data that encapsulated the challenges that the field faces.

Writer Identification Using GMM Supervectors and Exemplar-SVMs

Abstract This paper describes a method for robust offline writer identification. We propose to use RootSIFT descriptors computed densely at the script contours. GMM supervectors are used as encoding method to describe the characteristic handwriting of an individual scribe. GMM supervectors are created by adapting a background model to the distribution of local feature descriptors. Finally, we propose to use Exemplar-SVMs to train a document-specific similarity measure. We evaluate the method on three publicly available datasets (ICDAR / CVL / KHATT) and show that our method sets new performance standards on all three datasets. Additionally, we compare different feature sampling strategies as well as other encoding methods.

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.

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.

An automatic version of a reading disorder test

We present a novel system to automatically diagnose reading disorders. The system is based on a speech recognition engine with a module for prosodic analysis. The reading disorder test is based on eight different subtests. In each of the subtests, the system achieves a recognition accuracy of at least 95%. As in the perceptual version of the test, the results of the subtests are then joined into a final test result to determine whether the child has a reading disorder. In the final classification stage, the system identifies 98.3% of the 120 children correctly. In the future, our system will facilitate the clinical evaluation of reading disorders.

Are men more sleepy than women or does it only look like — Automatic analysis of sleepy speech

The degree of sleepiness in the Sleepy Language Corpus from the Interspeech 2011 Speaker State Challenge is predicted with regression and a very large feature vector. Most notable is the great gender difference which can mainly be attributed to females showing their sleepiness less than males do.