Sustained Vowel Phonation Research Articles

A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative states

Many research articles have explored the impact of surgical interventions on voice and speech evaluations, but advances are limited by the lack of publicly accessible datasets. To address this, a comprehensive corpus of 107 Spanish Castilian speakers was recorded, including control speakers and patients who underwent upper airway surgeries such as Tonsillectomy, Functional Endoscopic Sinus Surgery, and Septoplasty. The dataset contains 3,800 audio files, averaging 35.51 ± 5.91 recordings per patient. This resource enables systematic investigation of the effects of upper respiratory tract surgery on voice and speech. Previous studies using this corpus have shown no relevant changes in key acoustic parameters for sustained vowel phonation, consistent with initial hypotheses. However, the analysis of speech recordings, particularly nasalised segments, remains open for further research. Additionally, this dataset facilitates the study of the impact of upper airway surgery on speaker recognition and identification methods, and testing of anti-spoofing methodologies for improved robustness.

Acoustic COVID-19 Detection Using Multiple Instance Learning.

In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnostic tool for audio recordings could enable widespread testing at low costs. In order to achieve comparability between such algorithms, the DiCOVA challenge was created. It is based on the Coswara dataset offering the recording categories cough, speech, breath and vowel phonation. Recording durations vary greatly, ranging from one second to over a minute. A base model is pre-trained on random, short time intervals. Subsequently, a Multiple Instance Learning (MIL) model based on self-attention is incorporated to make collective predictions for multiple time segments within each audio recording, taking advantage of longer durations. In order to compete in the fusion category of the DiCOVA challenge, we utilize a linear regression approach among other fusion methods to combine predictions from the most successful models associated with each sound modality. The application of the MIL approach significantly improves generalizability, leading to an AUC ROC score of 86.6% in the fusion category. By incorporating previously unused data, including the sound modality 'sustained vowel phonation' and patient metadata, we were able to significantly improve our previous results reaching a score of 92.2%.

The Influence of Multisensory Input On Voice Perception and Production Using Immersive Virtual Reality

The purpose was to examine the influence of auditory vs visual vs combined audiovisual input on perception and production of one's own voice, using immersive virtual reality technology. Thirty-one vocally healthy men and women were investigated under 18 sensory input conditions, using immersive virtual reality technology. Conditions included two auditory rooms with varying reverberation times, two visual rooms with varying volumes, and the combination of audiovisual conditions. All conditions were repeated with and without background noise. Speech tasks included counting, sustained vowel phonation, an all-voiced sentence from the Consensus Auditory-Perceptual Evaluation of Voice, and the first sentence from the Rainbow Passage, randomly ordered. Perception outcome measures were participants' self-reported perceptions of their vocal loudness, vocal effort, and vocal comfort in speech. Production outcome measures were sound pressure level (SPL) and spectral moments (spectral mean and standard deviation in Hz, skewness, and kurtosis). Statistical analyses used self-reported vocal effort, vocal loudness, and vocal comfort in percent (0 = "not at all," 100 = extremely), SPL in dB, and spectral moments in Hz. The reference level was a baseline audiovisual deprivation condition. Results suggested (i) increased self-perceived vocal loudness and effort, and decreased comfort, with increasing room volume, speaker-to-listener distance, audiovisual input, and background noise, and (ii) increased SPL and fluctuations in spectral moments across conditions. Not only auditory, but also visual and audiovisual input influenced voice perception and production in ways that have not been previously documented. Findings contribute to the basic science understanding the role of visual, audiovisual and auditory input in voice perception and production, and also to models of voice training and therapy. The findings also set the foundation for the use of virtual reality in voice and speech training, as a potentially power solution to the generalization problem.

Are smartphones and low-cost external microphones comparable for measuring time-domain acoustic parameters?

This study examined and compared the diagnostic accuracy and correlation levels of the acoustic parameters of the audio recordings obtained from smartphones on two operating systems and from dynamic and condenser types of external microphones. The study included 87 adults: 57 with voice disorder and 30 with a healthy voice. Each participant was asked to perform a sustained vowel phonation (/a/). The recordings were taken simultaneously using five microphones AKG-P220, Shure-SM58, Samson Go Mic, Apple iPhone 6, and Samsung Galaxy J7 Pro microphones in an acoustically insulated cabinet. Acoustic examinations were performed using Praat version 6.2.09. The data were examined using Pearson correlation and receiver-operating characteristic (ROC) analyses. The parameters with the highest area under curve (AUC) values among all microphone recordings in the time-domain analyses were the frequency perturbation parameters. Additionally, considering the correlation coefficients obtained by synchronizing the microphones with each other and the AUC values together, the parameter with the highest correlation coefficient and diagnostic accuracy values was the jitter-local parameter. Period-to-period perturbation parameters obtained from audio recordings made with smartphones show similar levels of diagnostic accuracy to external microphones used in clinical conditions.

Effects of Speech and Language Therapists’ Personality Traits and Cognitive Emotion Regulation on the Perceptual Evaluation of Dysphonia

As in many healthcare fields, it is essential to establish clinical standards for voice assessments; however, some individual characteristics of clinicians, such as their personalities, may affect assessment reliability. Therefore, this study aims to examine the relationship between the personality traits and cognitive emotion regulation strategies of speech-language therapists (SLTs) and their GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) scoring. The study group comprised 33 SLTs and 9 women with dysphonia. The Five-Factor Personality Inventory (FFPI) and Cognitive Emotion Regulation Questionnaire (CERQ) were used to assess SLTs' psychological traits. The SLTs evaluated the acoustic recordings of sustained vowel phonation /a/ obtained from individuals with dysphonia, using the GRBAS scale. Cronbach's alpha was used to assess inter-rater reliability, and Spearman's correlation analysis was used to analyze the relationships among the variables. The findings revealed that the FFPI dimensions were weakly and significantly related to Grade (G) and Strain (S). In the maladaptive dimensions, with an increase in the scores of the neuroticism dimension of FPPI and the catastrophizing dimension of CERQ, the SLTs gave significantly higher scores to Grade (G). In addition, with an increase in the score of rumination or focus on thought, the SLTs perceived Strain (S) more negatively. Consistent with these findings, in the adaptive dimensions, the SLTs gave higher scores to Grade (G) as the positive refocusing and positive reappraisal scores decreased. The findings of this study showed that, as some maladaptive aspects of personality and cognitive emotion regulation increased and adaptive aspects decreased, the SLTs' perceptions of Grade (G) and Strain (S) became more negative. Additionally, these results suggested the possibility of advanced confounding variables beyond primary factors, such as training and experience in the perceptual voice assessment of the SLTs, and the importance of standardizing professional practices, such as the observation and supervision of experts.

Has machine learning over-promised in healthcare?: A critical analysis and a proposal for improved evaluation, with evidence from Parkinson’s disease

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community’s failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson’s disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

Comparison of Acoustic Voice Features Derived From Mobile Devices and Studio Microphone Recordings

Improvements in mobile device technology offer new opportunities for remote monitoring of voice for home and clinical assessment. However, there is a need to establish equivalence between features derived from signals recorded from mobile devices and gold standard microphone-preamplifiers. In this study acoustic voice features from android smartphone, tablet, and microphone-preamplifier recordings were compared. Data were recorded from 37 volunteers (20 female) with no history of speech disorder and six volunteers with Huntington's disease (HD) during sustained vowel (SV) phonation, reading passage (RP), and five syllable repetition (SR) tasks. The following features were estimated: fundamental frequency median and standard deviation (F0 and SD F0), harmonics-to-noise ratio (HNR), local jitter, relative average perturbation of jitter (RAP), five-point period perturbation quotient (PPQ5), difference of differences of amplitude and periods (DDA and DDP), shimmer, and amplitude perturbation quotients (APQ3, APQ5, and APQ11). Bland-Altman analysis revealed good agreement between microphone and mobile devices for fundamental frequency, jitter, RAP, PPQ5, and DDP during all tasks and a bias for HNR, shimmer and its variants (APQ3, APQ5, APQ11, and DDA). Significant differences were observed between devices for HNR, shimmer, and its variants for all tasks. High correlation was observed between devices for all features, except SD F0 for RP. Similar results were observed in the HD group for SV and SR task. Biological sex had a significant effect on F0 and HNR during all tests, and for jitter, RAP, PPQ5, DDP, and shimmer for RP and SR. No significant effect of age was observed. Mobile devices provided good agreement with state of the art, high-quality microphones during structured speech tasks for features derived from frequency components of the audio recordings. Caution should be taken when estimating HNR, shimmer and its variants from recordings made with mobile devices.

Profiles and predictors of onset based differences in vocal characteristics of adults with auditory neuropathy spectrum disorder (ANSD)

PurposeOnset-based differences are understudied in Auditory Neuropathy Spectrum Disorder (ANSD) in dimensions such as voice, which is addressed in the study. The study aimed to profile and predict the best metrics of onset-related differences in acoustic vocal characteristics of early and late-onset ANSD patients. Methods31 participants (15 early and 16 late-onset) aged 15–30 years diagnosed with ANSD were included in the study. The sustained phonation of vowel /i/ recorded by the participants using android based smartphones of selected configuration was sent over email to the experimenter. Acoustic parameters (fundamental frequency, harmonic frequencies, jitter, shimmer, harmonic-to-noise ratio, cepstral peak prominence -CPP, and pitch sigma) were analysed using Praat software. ResultsResults revealed significantly increased (p < 0.05) fundamental frequency along with decreased F2 and F3 of /i/ in the early-onset ANSD compared to the late-onset group, which can be explained based on differences in the pathophysiology of the disorder. Although not statistically significant, mean perturbations (jitter and shimmer), harmonic-to-noise ratio, cepstral peak prominence, and pitch sigma were more affected in the early-onset group, reflective of lowered auditory feedback and periodicity in their voice samples. Results of discriminant analysis marked the emergence of F2, F3, and CPP as the most sensitive metrics for onset-based group differences in voice characteristics. ConclusionsThe findings from the study highlight the role of acoustical voice evaluation (especially CPP, F2 & F3) in verifying the onset of ANSD disorder. The insights from the onset-based differences seen in vocal characteristics can indirectly help audiologists in deciding the management options for ANSD.

A Comparison of Healthy and Disordered Voices Using Multi-Dimensional Voice Program, Praat, and TF32

Instrumental voice assessment plays a critical role in identifying vocal issues and for documenting treatment outcomes. The reported voice data, however, are sensitive to the algorithm used by each acoustic analysis software program (AASP) to analyze the corresponding waveform. In the present study, five acoustic measures were compared across healthy speakers and speakers with dysphonia for three AASPs commonly used in research, education, and clinical practice: Multidimensional Voice Program (MDVP) by Computerized Speech Lab, Praat, and TF32. Sustained vowel phonations for the quantal vowels /ɑ/, /i/, and /u/ were analyzed for 80 speakers with organic dysphonia and 60 age- and sex-matched healthy controls. Descriptive, inferential, and correlation data are reported for mean fundamental frequency (mean F0), standard deviation of fundamental frequency (SD F0), short-term perturbation measures of jitter and shimmer, and harmonic-to-noise ratio (HNR). The present study replicated previous findings of interprogram differences for healthy speakers, with MDVP consistently yielding higher values than Praat and TF32 for SD F0, jitter, and shimmer and lower values for HNR. Similar, but magnified patterns of results were observed for speakers with dysphonia. The variation observed across programs calls into question the validity in comparing voice outcomes reported by one AASP to those previously obtained by another, particularly for acoustic signals with aperiodic components that are commonly present in disordered voices. It is advised that waveforms be visually inspected prior to conducting acoustic analysis, and that voice outcomes not be combined or compared across AASPs.

Segmentation of Glottal Images from High-Speed Videoendoscopy Optimized by Synchronous Acoustic Recordings.

Laryngeal high-speed videoendoscopy (LHSV) is an imaging technique offering novel visualization quality of the vibratory activity of the vocal folds. However, in most image analysis methods, the interaction of the medical personnel and access to ground truth annotations are required to achieve accurate detection of vocal folds edges. In our fully automatic method, we combine video and acoustic data that are synchronously recorded during the laryngeal endoscopy. We show that the image segmentation algorithm of the glottal area can be optimized by matching the Fourier spectra of the pre-processed video and the spectra of the acoustic recording during the phonation of sustained vowel /i:/. We verify our method on a set of LHSV recordings taken from subjects with normophonic voice and patients with voice disorders due to glottal insufficiency. We show that the computed geometric indices of the glottal area make it possible to discriminate between normal and pathologic voices. The median of the Open Quotient and Minimal Relative Glottal Area values for healthy subjects were 0.69 and 0.06, respectively, while for dysphonic subjects were 1 and 0.35, respectively. We also validate these results using independent phoniatrician experts.

Continuous theta burst stimulation over left and right supramarginal gyri demonstrates their involvement in auditory feedback control of vocal production.

The supramarginal gyrus (SMG) has been implicated in auditory-motor integration for vocal production. However, whether the SMG is bilaterally or unilaterally involved in auditory feedback control of vocal production in a causal manner remains unclear. The present event-related potential (ERP) study investigated the causal roles of the left and right SMG to auditory-vocal integration using neuronavigated continuous theta burst stimulation (c-TBS). Twenty-four young adults produced sustained vowel phonations and heard their voice unexpectedly pitch-shifted by ±200 cents after receiving active or sham c-TBS over the left or right SMG. As compared to sham stimulation, c-TBS over the left or right SMG led to significantly smaller vocal compensations for pitch perturbations that were accompanied by smaller cortical P2 responses. Moreover, no significant differences were found in the vocal and ERP responses when comparing active c-TBS over the left vs. right SMG. These findings provide neurobehavioral evidence for a causal influence of both the left and right SMG on auditory feedback control of vocal production. Decreased vocal compensations paralleled by reduced P2 responses following c-TBS over the bilateral SMG support their roles for auditory-motor transformation in a bottom-up manner: receiving auditory feedback information and mediating vocal compensations for feedback errors.

Gender- and Age- Stratified Normative Voice Data in Japanese-Speaking Subjects: Analysis of Sustained Habitual Phonations

There is no normative voice dataset for Japanese speakers in the English literature. We constructed age- and gender-stratified normative voice data with the assistance of vocally healthy Japanese speakers. A total of 111 vocally healthy Japanese speakers (42 men, 69 women) were divided into young (13 men, 30 women), middle-aged (18 men, 27 women), and elderly (11 men, 12 women) groups. Participants underwent aerodynamic, acoustic, and audio-perceptual studies of sustained habitual vowel phonations, and the obtained data were statistically analyzed in terms of age and gender. Both gender- and age-related differences were noted in fundamental frequencies, sound pressure level, shimmer, and amplitude perturbation quotient, while only gender-related differences were noted in mean flow rate and only age-related changes were observed in subglottal pressure; laryngeal resistance; and G, R, B, and S scores of the GRBAS scale. The gender- and age-related difference data were comparable with the reported data in other languages, ethnicities, or countries. The present study is the first to provide a database of normative voice data of Japanese speakers. The idiosyncrasy of Japanese is considered minor in sustained habitual vowel phonations.

Validation of cepstral peak prominence in assessing early voice changes of Parkinson's disease: Effect of speaking task and ambient noise.

Although the cepstral peak prominence (CPP) and its variant, the cepstral peak prominence smooth (CPPS), are considered to be robust acoustic measures for the evaluation of dysphonia, whether they are sensitive to capture early voice changes in Parkinson's disease (PD) has not yet been explored. This study aimed to investigate the voice changes via the CPP measures in the idiopathic rapid eye movement sleep behavior disorder (iRBD), a special case of prodromal neurodegeneration, and recently diagnosed and advanced-stage Parkinson's disease (AS-PD) patients using different speaking tasks across noise-free and noisy environments. The sustained vowel phonation, reading of passages, and monologues of 60 early stage untreated PD, 30 advanced-stage Parkinson's disease, 60 iRBD, and 60 healthy control (HC) participants were evaluated. Significant differences were found between the PD groups and controls in sustained phonation via the CPP (p < 0.05) and CPPS (p < 0.01) and the monologue via the CPP (p < 0.01), although neither the CPP nor CPPS measures were sufficiently sensitive to capture the possible prodromal dysphonia in the iRBD. The quality of the CPP and CPPS measures was influenced substantially by the addition of ambient noise. It was anticipated that the CPP measures might serve as a promising digital biomarker in assessing the dysphonia from the early stages of PD.

Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients

Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.

Intensive voice treatment (the Lee Silverman Voice Treatment [LSVT®LOUD]) for individuals with Wilson’s disease and adult cerebral palsy: two case reports

Objective: In this case report, we aimed to examine the effects of an intensive voice treatment (the Lee Silverman Voice Treatment [LSVT®LOUD]) for Wilson’s disease (WD), and adult cerebral palsy (CP), and dysarthria. Method: The participants received LSVT®LOUD four times a week for 4 weeks. Acoustic, perceptual (GRBAS) analyses were performed and data from the Voice Handicap Index (VHI) were obtained before and after treatment. Results: Besides the Harmonics-to Noise Ratio (HNR) value (dB) of the participant with WD, for both participants’ fundamental frequencies (Hz), jitter (%), and shimmer (%) values showed significant differences (p < .05) after therapy. Both participants showed significant improvements (p < .05) in the duration (s) and the sound pressure level (dB, SPL) of sustained vowel phonation (/a/), in SPL (dB) of pitch range (high and low /a/) and reading and conversation (p < .01). There was a positive improvement in the high-frequency values (Hz) of both participants but not in the low-frequency values (Hz) in the participant with WD. Perceptual analysis with GRBAS judgements of sustained vowel (/a/) and paragraph reading of two participants also showed improvement. After therapy, perceived loudness of the participants’ voice increased. Conclusions: The findings provide some preliminary observations that the individuals with WD and the adult individuals with CP can respond positively to intensive speech treatment such as LSVT®LOUD. Further studies are needed to investigate speech treatments specific to WD and adult CP.

Objective vowel sound characteristics and their relationship with motor dysfunction in Asian Parkinson's disease patients

BackgroundSpeech impairments are very common in patients with Parkinson's disease (PD). However, knowledge of their objective characteristics and relationship to other motor symptoms amongst Asian PD patients is limited. ObjectivesTo identify objective vowel sound characteristics in Thai PD patients and correlate with disease severity, as determined by UPDRS and various sub-scores. MethodWe evaluated 100 Thai PD patients, with a mean age of 66.56 years (±7.52) and HY of 2.7 (±1.08), and 101 age-matched controls. Phonatory evaluation, comprising of 15 objective parameters, was conducted using the Multi-Dimensional Voice Programme with a sustained /a/ phonation. ResultsPD patients exhibited significantly higher values of all dimensions of the phonatory parameters evaluated compared to controls (All, p < 0.001) except for duration of sustained phonation, which was significantly shorter in PD patients. When early- and advanced-stage patients were compared, significantly different parameters were limited to frequency perturbation parameters (Jitt, p = 0.01; RAP, p = 0.013; PPQ, p = 0.01; sPPQ, p = 0.001; vF0, p = 0.011), and NHR (p = 0.028). Several significant and moderate correlations were observed between both STD and frequency perturbation parameters and UPDRS-III, bradykinesia sub-score, and gait and postural instability sub-score. Both vF0, and STD significantly correlated with UPDRS-III and sub-scores in advanced stage patients. ConclusionOur study provides objective evidence of phonatory dysfunction in Asian PD patients with certain characteristics correlated with advanced stage or different motor dysfunction. Sustained vowel phonation is a promising digital outcome for global phenotyping a large number of PD patients.

Non-negative matrix factorization-based time-frequency feature extraction of voice signal for Parkinson's disease prediction

Parkinson's disease (PD) is a neuron related disorder that affects the people in old age. The majority of people suffering from PD develop several voice impairments mainly related to what is known as dysarthric speech. Voice analysis can help in PD detection and in the evaluation of the dysarthria level of the patients. This study introduces time-frequency features to model discontinuities and abrupt changes that arise in the voice signal due to PD. The proposed method consists of four stages: time-frequency matrix (TFM) representation, TFM decomposition using non-negative matrix factorization (NMF), feature extraction and classification. Statistical analyses show that the proposed time-frequency features significantly differentiate between PD patients and healthy speakers. Experiments with sustained vowel phonations and isolated words of the corpus PC–GITA are conducted. The proposed method achieved average classification accuracies of up to 92% in vowels, and 97% in words. There is an improvement in accuracy ranging from 10% to 40% compared to existing methods. Further, the developed models are evaluated upon an independent dataset. Results on this separate test set show accuracies ranging from 63% to 75% in vowels, and from 53% to 75% in isolated words. Regarding the dysarthria level evaluation, Spearman's correlations between original and predicted labels are around 0.81 in sustained vowels and in isolated words. The results indicate that the proposed approach is suitable and robust for the automatic detection of PD.

Analysis of Speech Fundamental Frequencies for Different Tasks in Japanese

Speech fundamental frequency (SFF) assessment is essential for all dysphonia patients to effectively evaluate the therapeutic effects of voice therapy, especially in patients with disturbances in their voice pitch due to mutational dysphonia, Reinke's edema, or as side effects of hormone therapy. A standard method of SFF measurement remains unknown. Speech tasks such as sustained vowel phonation, counting, reading passage, and spontaneous speech have generally been used for SFF measurements. Ideally, spontaneous speech best reflects SFF; however, this task has not yet been clearly defined and is limited with regard to its adaptation to a clinical setting. A reliable task for SFF measurement in Japanese, which corresponds to a speech task that most closely reflects the value that would be observed with typical spontaneous speech, has not been investigated. This study aimed to identify a reliable speech task by measuring the SFF values elicited by different widely used speech tasks in Japanese, and assess its reliability and coefficient of determination (R2). Sixty healthy volunteers (30 men and 30 women; aged 19-30 years; mean age 22.5 years) were enrolled. All experimental procedures were performed in Japanese. The SFF values for the speech tasks were determined through the voice samples recorded using a Pulse Code Modulation (PCM) recorder. Each task, except spontaneous speech, was repeated five times, and the average fundamental frequency in each task was determined as the SFF. To assess the reliability of the SFF values across daily variations within individual speakers, the SFF measurements were repeated on two different days, separated by at least 1 week. The SFF values of sustained /a/ phonation, sustained vowel-average, counting, reading passage, and spontaneous speech had excellent reliability, in terms of their reproduction based on intraclass correlation. Significantly high SFF values were observed, in decreasing order, for sustained vowels-average, counting, reading passage, and spontaneous speech in both males and females. The highest R2 for spontaneous speech was that of reading passage in both males (R2=0.771) and females (R2=0.806) (P < 0.01). When spontaneous speech was presented as a task most reflective of daily conversation, reading passage was determined to be the reliable task to assess the therapeutic effect of voice therapy in Japanese.

Effect of paan masala on voice parameters

Introduction: Ingestion of paan masala (a mixture of areca nut with catechu, slaked lime, and added flavoring agents) is one of the means of smokeless tobacco usage which can also have adverse effect on voice such as dryness in vocal folds which in turn may result in depletion of mucosal layer of the vocal folds. Aim: Aim of the study was to investigate the consequence of paan masala usage on voice parameters and to compare the acoustic parameters amid two groups who were paan users and non paan users respectively. Method: A total of 120 samples (3 trials of phonation of sustained vowel /a/) were obtained from male participants between age ranges of 18 – 40 years. 60 recordings were taken from 20 individuals who indulged in intake of paan masala from past 5-10 years. Another 60 recordings were from 20 individuals who were non paan masala users. The recorded samples were subjected to two types of analysis viz. perceptual evaluation was carried out by 10 qualified speech language pathologists based on standardized voice rating scale and acoustic analysis was done using clinically acceptable software. Results: Results of perceptual analysis indicated increased breathiness which was supported by acoustical NNE. Greater values in jitter, shimmer and Normalized Noise Energy (NNE) in paan users indicates of being at risk of having vocal pathologies. These parameters of voice had a significant difference between paan users and individuals who do not consume paan. Conclusion: The results do indicate that consumption of paan poses an impending threat to voice which is evident in abnormal acoustical findings and it is essential for people to be aware of the consequences of devouring paan for mere pleasure of minutes.

Classification of ALS patients based on acoustic analysis of sustained vowel phonations

Amyotrophic lateral sclerosis (ALS) is incurable neurological disorder with rapidly progressive course. Common early symptoms of ALS are difficulty in swallowing and speech. However, early acoustic manifestation of speech and voice symptoms is very variable, that making their detection very challenging, both by human specialists and automatic systems. This study presents an approach to voice assessment for automatic system that separates healthy people from patients with ALS. In particular, this work focus on analysing of sustain phonation of vowels /a/ and/i/ to perform automatic classification of ALS patients. A wide range of acoustic features such as MFCC, formants, jitter, shimmer, vibrato, PPE, GNE, HNR, etc. were analysed. We also proposed a new set of acoustic features for characterizing harmonic structure of the vowels. Calculation of these features is based on pitch synchronized voice analysis. A linear discriminant analysis (LDA) was used to classify the phonation produced by patients with ALS and those by healthy individuals. Several algorithms of feature selection were tested to find optimal feature subset for LDA model. The study’s experiments show that the most successful LDA model based on 32 features picked out by LASSO feature selection algorithm attains 99.7% accuracy with 99.3% sensitivity and 99.9% specificity. Among the classifiers with a small number of features, we can highlight LDA model with 5 features, which has 89.0% accuracy (87.5% sensitivity and 90.4% specificity).