Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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Spectral analysis of pathological acoustic speech waveforms

Biomedical engineering is the application of engineering principles and techniques to the medical field. The design and problem solving skills of engineering are combined with medical and biological science, which improves medical disorder diagnosis and treatment. The purpose of this study is to develop an automated procedure for detecting excessive jitter in speech signals, which is useful for differentiating normal from pathologic speech. The fundamental motivation for this research is that tools are needed by speech pathologists and laryngologists for use in the early detection and treatment of laryngeal disorders. Acoustical analysis of speech was performed to analyze various features of a speech signal. Earlier research established a relation between pitch period jitter and harmonic bandwidth. This concept was used for detecting laryngeal disorders in speech since pathologic speech has been found to have larger amounts of jitter than normal speech. Our study was performed using vowel samples from the voice disorder database recorded at the Massachusetts Eye and Ear Infirmary (MEEI) in1994. The KAYPENTAX company markets this database. Software development was conducted using MATLAB, a user-friendly programming language which has been applied widely for signal processing. An algorithm was developed to compute harmonic bandwidths for various speech samples of sustained vowel sounds. Open and closed tests were conducted on 23 samples of pathologic and normal speech samples each. Classification results showed 69.56% probability of correct detection of pathologic speech samples during an open test

A Practical Approach: Design and Implementation of a Healthcare Software for Screening of Dysphonic Patients

Risk management in the development of medical software and devices is one of the most crucial processes in ensuring accurate diagnoses and treatment of disease. The consequences of wrong decisions that happen in our daily life might be unembellished. However, wrong decisions in healthcare based on unreliable evidence due to erroneous software could result in loss of life. Dysphonic patients suffering from various vocal fold disorders might have a threat of life due to inaccurate diagnosis. Some voice disorders, such as keratosis, are precancerous, and can become cancerous in cases that involve inaccurate diagnosis due to software failure. The objective of this paper is to design and implement a healthcare software for the detection of voice disorders in nonperiodic speech signals. Occurrences of potential risks during the design and development of the proposed software are taken into account to avoid failure. The software is implemented by applying the local binary pattern (LBP) operator on the textures of nonperiodic signals. The textures are obtained through the recurrence plot. The LBP operator computes the histograms for normal persons and dysphonic patients, and these histograms are used with the support vector machine for the automatic classification of dysphonic patients. The software is evaluated and tested by using the Massachusetts Eye and Ear Infirmary voice disorder database. The success rate of the proposed healthcare system is 97.73% ± 1.2, and the area under the receiver operating characteristic curve is 0.98 ± 0. The performance of the proposed healthcare system is much better than the existing commercial software used for screening dysphonic patients

Exploring the impact of data poisoning attacks on machine learning model reliability

Recent years have seen the widespread adoption of Artificial Intelligence techniques in several domains, including healthcare, justice, assisted driving and Natural Language Processing (NLP) based applications (e.g., the Fake News detection). Those mentioned are just a few examples of some domains that are particularly critical and sensitive to the reliability of the adopted machine learning systems. Therefore, several Artificial Intelligence approaches were adopted as support to realize easy and reliable solutions aimed at improving the early diagnosis, personalized treatment, remote patient monitoring and better decision-making with a consequent reduction of healthcare costs. Recent studies have shown that these techniques are venerable to attacks by adversaries at phases of artificial intelligence. Poisoned data set are the most common attack to the reliability of Artificial Intelligence approaches. Noise, for example, can have a significant impact on the overall performance of a machine learning model. This study discusses the strength of impact of noise on classification algorithms. In detail, the reliability of several machine learning techniques to distinguish correctly pathological and healthy voices by analysing poisoning data was evaluated. Voice samples selected by available database, widely used in research sector, the Saarbruecken Voice Database, were processed and analysed to evaluate the resilience and classification accuracy of these techniques. All analyses are evaluated in terms of accuracy, specificity, sensitivity, F1-score and ROC area

Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project