Low band spectral tilt analysis for pathological voice discrimination

This paper presents a new method for discriminating between subjects with healthy voices and subjects with diseases in the vocal folds. This method uses speech signals and spectral analysis of the sustained vowel /a/. The slope between a first band of the signal defined in the first two harmonics and a second band defined in the zone of the /a/ first formant contains information that allows to correctly classify the database of pathological voices of the University of Sao Paulo. The presented method can be applied in the direct analysis of spectra or implemented in high-level classifiers as a complement to other parameters.info:eu-repo/semantics/publishedVersio

A new database of healthy and pathological voices☆ Ugo Cesari a, Giuseppe De Pietro b, Elio Marciano c, Ciro Niri d, Giovanna Sannino,b, Laura Verde e a Department of Otorhinolaryngology, University Hospital (Policlinico) Federico II of Naples, Via S.Pansini, 5 Naples, Italy b Institute of High Performance Computing and Networking (ICAR-CNR), Via Pietro Castellino, 111, Naples, Italy c Area of Audiology, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via S.Pansini, 5, Naples, Italy d Independent Doctor Surgeon Specialized in Audiology and Phoniatrics, Naples, Italy e Department of Engineering, University of Naples Parthenope, Centro Direzionale di Napoli, Isola C4, Naples, Italy

In the era of Edge-of-Things computing for the accomplishment of smart healthcare systems, the availability of accurate and reliable databases is important to provide the right tools for researchers and business companies to design, develop and test new techniques, methodologies and/or algorithms to monitor or detect the patient’s healthcare status. In this paper, the study and building of the VOice ICar fEDerico II (VOICED) database are presented, useful for anybody who needs voice signals in her/his research activities. It consists of 208 healthy and pathological voices collected during a clinical study performed following the guidelines of the medical SIFEL (Società Italiana di Foniatria e Logopedia) protocol and the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement. For each subject, the database contains a recording of the vowel /a/ of five seconds in length, lifestyle information, the medical diagnosis, and the results of two specific medical questionnaires

Objective dysphonia quantification in vocal fold paralysis: comparing nonlinear with classical measures

Clinical acoustic voice recording analysis is usually performed using classical perturbation measures including jitter, shimmer and noise-to-harmonic ratios. However, restrictive mathematical limitations of these measures prevent analysis for severely dysphonic voices. Previous studies of alternative nonlinear random measures addressed wide varieties of vocal pathologies. Here, we analyze a single vocal pathology cohort, testing the performance of these alternative measures alongside classical measures.

We present voice analysis pre- and post-operatively in unilateral vocal fold paralysis (UVFP) patients and healthy controls, patients undergoing standard medialisation thyroplasty surgery, using jitter, shimmer and noise-to-harmonic ratio (NHR), and nonlinear recurrence period density entropy (RPDE), detrended fluctuation analysis (DFA) and correlation dimension. Systematizing the preparative editing of the recordings, we found that the novel measures were more stable and hence reliable, than the classical measures, on healthy controls.

RPDE and jitter are sensitive to improvements pre- to post-operation. Shimmer, NHR and DFA showed no significant change (p > 0.05). All measures detect statistically significant and clinically important differences between controls and patients, both treated and untreated (p < 0.001, AUC > 0.7). Pre- to post-operation, GRBAS ratings show statistically significant and clinically important improvement in overall dysphonia grade (G) (AUC = 0.946, p < 0.001).

Re-calculating AUCs from other study data, we compare these results in terms of clinical importance. We conclude that, when preparative editing is systematized, nonlinear random measures may be useful UVFP treatment effectiveness monitoring tools, and there may be applications for other forms of dysphonia.
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Automatic Detection of Laryngeal Pathology on Sustained Vowels Using Short-Term Cepstral Parameters: Analysis of Performance and Theoretical Justification

The majority of speech signal analysis procedures for automatic detection of laryngeal pathologies mainly rely on parameters extracted from time domain processing. Moreover, calculation of these parameters often requires prior pitch period estimation; therefore, their validity heavily depends on the robustness of pitch detection. Within this paper, an alternative approach based on cepstral- domain processing is presented which has the advantage of not requiring pitch estimation, thus providing a gain in both simplicity and robustness. While the proposed scheme is similar to solutions based on Mel-frequency cepstral parameters, already present in literature, it has an easier physical interpretation while achieving similar performance standards

A database and digital signal processing framework for the perceptual analysis of voice quality

Bermúdez de Alvear RM, Corral J, Tardón LJ, Barbancho AM, Fernández Contreras E, Rando Márquez S, Martínez-Arquero AG, Barbancho I. A database and digital signal processing framework for the perceptual analysis of voice quality. Pan European Voice Conferenc: PEVOC 11 Abstract Book. Aug. 31-Sept.2, 2015.Introduction. Clinical assessment of dysphonia relies on perceptual as much as instrumental methods of analysis [1]. The perceptual auditory analysis is potentially subject to several internal and external sources of bias [2]. Furthermore acoustic analyses which have been used to objectively characterize pathological voices are likely to be affected by confusion variables such as the signal processing or the hardware and software specifications [3]. For these reasons the poor correlation between perceptual ratings and acoustic measures remains to be a controversial matter [4]. The availability of annotated databases of voice samples is therefore of main importance for clinical and research purposes. Databases to perform digital processing of the vocal signal are usually built from English speaking subjects’ sustained vowels [5]. However phonemes vary from one language to another and to the best of our knowledge there are no annotated databases with Spanish sustained vowels from healthy or dysphonic voices. This work shows our first steps to fill in this gap. For the aim of aiding clinicians and researchers in the perceptual assessment of voice quality a two-fold objective was attained. On the one hand a database of healthy and disordered Spanish voices was developed; on the other an automatic analysis scheme was accomplished on the basis of signal processing algorithms and supervised learning machine techniques. Material and methods. A preliminary annotated database was created with 119 recordings of the sustained Spanish /a/; they were perceptually labeled by three experienced experts in vocal quality analysis. It is freely available under Links in the ATIC website (www.atic.uma.es). Voice signals were recorded using a headset condenser cardioid microphone (AKG C-544 L) positioned at 5 cm from the speaker’s mouth commissure. Speakers were instructed to sustain the Spanish vowel /a/ for 4 seconds. The microphone was connected to a digital recorder Edirol R-09HR. Voice signals were digitized at 16 bits with 44100 Hz sampling rate. Afterwards the initial and last 0.5 second segments were cut and the 3 sec. mid portion was selected for acoustic analysis. Sennheiser HD219 headphones were used by judges to perceptually evaluate voice samples. To label these recordings raters used the Grade-Roughness-Breathiness (GRB) perceptual scale which is a modified version of the original Hirano’s GRBAS scale, posteriorly modified by Dejonckere et al., [6]. In order to improve intra- and inter-raters’ agreement two types of modifications were introduced in the rating procedure, i.e. the 0-3 points scale resolution was increased by adding subintervals to the standard 0-3 intervals, and judges were provided with a written protocol with explicit definitions about the subintervals boundaries. By this way judges could compensate for the potential instability that might occur in their internal representations due to the perceptual context influence [7]. Raters’ perceptual evaluations were simultaneously performed by means of connecting the Sennheiser HD219 headphones to a multi-channel headphone preamp Behringer HA4700 Powerplay Pro-XL. The Yin algorithm [8] was selected as initial front-end to identify voiced frames and extract their fundamental frequency. For the digital processing of voice signals some conventional acoustic parameters [6] were selected. To complete the analysis the Mel-Frequency Cepstral Coefficients (MFCC) were further calculated because they are based on the auditory model and they are thus closer to the auditory system response than conventional features. Results. In the perceptual evaluation excellent intra-raters agreement and very good inter-raters agreement were achieved. During the supervised machine learning stage some conventional features were found to attain unexpected low performance in the classification scheme selected. Mel Frequency Cepstral Coefficients were promising for assorting samples with normal or quasi-normal voice quality. Discussion and conclusions. Despite it is still small and unbalanced the present annotated data base of voice samples can provide a basis for the development of other databases and automatic classification tools. Other authors [9, 10, 11] also found that modeling the auditory non-linear response during signal processing can help develop objective measures that better correspond with perceptual data. However highly disordered voices classification remains to be a challenge for this set of features since they cannot be correctly assorted by either conventional variables or the auditory model based measures. Current results warrant further research in order to find out the usability of other types of voice samples and features for the automatic classification schemes. Different digital processing steps could be used to improve the classifiers performance. Additionally other types of classifiers could be taken into account in future studies. Acknowledgment. This work was funded by the Spanish Ministerio de Economía y Competitividad, Project No. TIN2013-47276-C6-2-R has been done in the Campus de Excelencia Internacional Andalucía Tech, Universidad de Málaga. References [1] Carding PN, Wilson JA, MacKenzie K, Deary IJ. Measuring voice outcomes: state of the science review. The Journal of Laryngology and Otology 2009;123,8:823-829. [2] Oates J. Auditory-perceptual evaluation of disordered voice quality: pros, cons and future directions. Folia Phoniatrica et Logopaedica 2009;61,1:49-56. [3] Maryn et al. Meta-analysis on acoustic voice quality measures. J Acoust Soc Am 2009; 126, 5: 2619-2634. [4] Vaz Freitas et al. Correlation Between Acoustic and Audio-Perceptual Measures. J Voice 2015;29,3:390.e1 [5] “Multi-Dimensional Voice Program (MDVP) Model 5105. Software Instruction Manual”, Kay PENTAX, A Division of PENTAX Medical Company, 2 Bridgewater Lane, Lincoln Park, NJ 07035-1488 USA, November 2007. [6] Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, Van De Heyning P, Remacle M, Woisard V. A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the Comm. on Phoniatrics of the European Laryngological Society (ELS). Eur Arch Otorhinolaryngol 2001;258:77–82. [7] Kreiman et al. Voice Quality Perception. J Speech Hear Res 1993;36:21-4 [8] De Cheveigné A, Kawahara H. YIN, a fundamental frequency estimator for speech and music. J. Acoust. Soc. Amer. 202; 111,4:1917. [9] Shrivastav et al. Measuring breathiness. J Acoust Soc Am 2003;114,4:2217-2224. [10] Saenz-Lechon et al. Automatic Assessment of voice quality according to the GRBAS scale. Eng Med Biol Soc Ann 2006;1:2478-2481. [11] Fredouille et al. Back-and-forth methodology for objective voice quality assessment: from/to expert knowledge to/from automatic classification of dysphonia. EURASIP J Appl Si Pr 2009.Campus de Excelencia Internacional Andalucía Tech, Universidad de Málaga. Ministerio de Economía y Competitividad, Projecto No. TIN2013-47276-C6-2-R

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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Characterization of Healthy and Pathological Voice Through Measures Based on Nonlinear Dynamics

In this paper, we propose to quantify the quality of the recorded voice through objective nonlinear measures. Quantification of speech signal quality has been traditionally carried out with linear techniques since the classical model of voice production is a linear approximation. Nevertheless, nonlinear behaviors in the voice production process have been shown. This paper studies the usefulness of six nonlinear chaotic measures based on nonlinear dynamics theory in the discrimination between two levels of voice quality: healthy and pathological. The studied measures are first- and second-order Renyi entropies, the correlation entropy and the correlation dimension. These measures were obtained from the speech signal in the phase-space domain. The values of the first minimum of mutual information function and Shannon entropy were also studied. Two databases were used to assess the usefulness of the measures: a multiquality database composed of four levels of voice quality (healthy voice and three levels of pathological voice); and a commercial database (MEEI Voice Disorders) composed of two levels of voice quality (healthy and pathological voices). A classifier based on standard neural networks was implemented in order to evaluate the measures proposed. Global success rates of 82.47% (multiquality database) and 99.69% (commercial database) were obtained.Publicad

Cepstral analysis of speech signals in the process of automatic pathological voice assessment

The paper describes the problem of cepstral speech analysis in the process of automated voicedisorder probability estimation. The author proposes to derive two of the most diagnosticallysignificant voice features: quality of harmonic structure and degree of subharmonic from cepstrumof speech signal. Traditionally, these attributes are estimated auricularly or by spectrum (orspectrogram) observation, hence this analysis often lacks accuracy and objectivity. The introducedparameters were calculated for the recordings from Disordered Voice Database (Kay, model 4337version 2.7.0) which consists of 710 voice samples (657 pathological, 53 healthy) recorded in thelaboratory environment and described with diagnosis and a number of additional attributes (suchas age, sex, nationality).The proposed cepstral voice features were compared to similar voice parameters derived fromMultidimensional Voice Program (Kay, model 5105 version 2.7.0) in respect to their diagnosticsignificance and presented graphically. The results show that cepstral features are more correlatedwith decision and better discriminate clusters of healthy and disordered voices. Additionally, bothparameters are obtained by single cepstral transform and do not require to perform F0 trackingearlier as it is derived simultaneously

Classification of three pathological voices based on specific features groups using support vector machine

Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area