Cepstrum-based voice measures, such as smoothed cepstral peak prominence (CPPS), are influenced by voice sound pressure level (SPL) in vocally healthy adults. Since it is unclear if similar effects hold in voice disordered adults and how these interact with natural fundamental frequency (fo) changes, this study examines voice SPL and fo effects on CPPS in women with vocal hyperfunction and vocally healthy controls.
Retrospective matched case-control study.
Fifty-eight women with vocal hyperfunction were individually matched with 58 vocally healthy women for occupation and approximate age. The patient group comprised women exhibiting phonotraumatic vocal hyperfunction associated with vocal fold nodules (n = 39) or polyps (n = 5), and nonphonotraumatic vocal hyperfunction associated with primary muscle tension dysphonia (n = 14). All participants sustained the vowel /a/ at soft, comfortable, and loud loudness conditions. Voice SPL, fo, and CPPS (dB) were computed from acoustic voice recordings using Praat. The effects of loudness condition, measured voice SPL, and fo on CPPS were assessed with linear mixed models. Pairwise correlations among voice SPL, fo, and CPPS were assessed using multiple regression analysis.
Increasing voice SPL correlated significantly (P < 0.001) with higher CPPS in both patient (r2 = 0.53) and normative groups (r2 = 0.45). fo had statistically significant effects on CPPS (P < 0.001), but with a weak relation for the patient (r2 = 0.02) and control groups (r2 = 0.05).
In women with and without voice disorder, CPPS is highly affected by the individual's voice SPL in vowel phonation. Future studies could investigate how these effects should be controlled for to improve the diagnostic value of acoustic-based cepstral measures.
The goal of this study was to employ frequently used analysis methods and tasks to identify values for cepstral peak prominence (CPP) that can aid clinical voice evaluation. Experiment 1 identified CPP values to distinguish speakers with and without voice disorders. Experiment 2 was an initial attempt to estimate auditory-perceptual ratings of overall dysphonia severity using CPP values.
CPP was computed using the Analysis of Dysphonia in Speech and Voice (ADSV) program and Praat. Experiment 1 included recordings from 295 patients with medically diagnosed voice disorders and 50 vocally healthy control speakers. Speakers produced sustained /a/ vowels and the English language Rainbow Passage. CPP cutoff values that best distinguished patient and control speakers were identified. Experiment 2 analyzed recordings from 32 English speakers with varying dysphonia severity and provided preliminary validation of the Experiment 1 cutoffs. Speakers sustained the /a/ vowel and read four sentences from the Consensus Auditory-Perceptual Evaluation of Voice protocol. Trained listeners provided auditory-perceptual ratings of overall dysphonia for the recordings, which were estimated using CPP values in a linear regression model whose performance was evaluated using the coefficient of determination (r2).
Experiment 1 identified CPP cutoff values of 11.46 dB (ADSV) and 14.45 dB (Praat) for the sustained /a/ vowels and 6.11 dB (ADSV) and 9.33 dB (Praat) for the Rainbow Passage. CPP values below those thresholds indicated the presence of a voice disorder with up to 94.5% accuracy. In Experiment 2, CPP values estimated ratings of overall dysphonia with r2 values up to .74.
The CPP cutoff values identified in Experiment 1 provide normative reference points for clinical voice evaluation based on sustained /a/ vowels and the Rainbow Passage. Experiment 2 provides an initial predictive framework that can be used to relate CPP values to the auditory perception of overall dysphonia severity based on sustained /a/ vowels and Consensus Auditory-Perceptual Evaluation of Voice sentences.
Given the established linear relationship between neck surface vibration magnitude and mean subglottal pressure (Ps) in vocally healthy speakers, the purpose of this study was to better understand the impact of the presence of a voice disorder on this baseline relationship.
Data were obtained from participants with voice disorders representing a variety of glottal conditions, including phonotraumatic vocal hyperfunction, nonphonotraumatic vocal hyperfunction, and unilateral vocal fold paralysis. Participants were asked to repeat /p/-vowel syllable strings from loud-to-soft loudness levels in multiple vowel contexts (/pa/, /pi/, /pu/) and pitch levels (comfortable, higher than comfortable, lower than comfortable). Three statistical metrics were computed to analyze the regression line between neck surface accelerometer (ACC) signal magnitude and Ps within and across pitch, vowel, and voice disorder category: coefficient of determination (r2), slope, and intercept. Three linear mixed-effects models were used to evaluate the impact of voice disorder category, pitch level, and vowel context on the relationship between ACC signal magnitude and Ps.
The relationship between ACC signal magnitude and Ps was statistically different in patients with voice disorders than in vocally healthy controls; patients exhibited higher levels of Ps given similar values of ACC signal magnitude. Negligible effects were found for pitch condition within each voice disorder category, and negligible-to-small effects were found for vowel context. The mean of patient-specific r2 values was .63, ranging from .13 to .92.
The baseline, linear relationship between ACC signal magnitude and Ps is affected by the presence of a voice disorder, with the relationship being participant-specific. Further work is needed to improve ACC-based prediction of Ps, across treatment, and during naturalistic speech production.
In vocally healthy children and adults, speaking voice loudness differences can significantly confound acoustic perturbation measurements. This study examines the effects of voice sound pressure level (SPL) on jitter, shimmer, and harmonics-to-noise ratio (HNR) in adults with voice disorders and a control group with normal vocal status.
This is a matched case-control study.
We assessed 58 adult female voice patients matched according to approximate age and occupation with 58 vocally healthy women. Diagnoses included vocal fold nodules (n = 39, 67.2%), polyps (n = 5, 8.6%), and muscle tension dysphonia (n = 14, 24.1%). All participants sustained the vowel /a/ at soft, comfortable, and loud phonation levels. Acoustic voice SPL, jitter, shimmer, and HNR were computed using Praat. The effects of loudness condition, voice SPL, pathology, differential diagnosis, age, and professional voice use level on acoustic perturbation measures were assessed using linear mixed models and Wilcoxon signed rank tests.
In both patient and normative control groups, increasing voice SPL correlated significantly (P < 0.001) with decreased jitter and shimmer, and increased HNR. Voice pathology and differential diagnosis were not linked to systematically higher jitter and shimmer. HNR levels, however, were statistically higher in the patient group than in the control group at comfortable phonation levels. Professional voice use level had a significant effect (P < 0.05) on jitter, shimmer, and HNR.
The clinical value of acoustic jitter, shimmer, and HNR may be limited if speaking voice SPL and professional voice use level effects are not controlled for. Future studies are warranted to investigate whether perturbation measures are useful clinical outcome metrics when controlling for these effects.
This study examined the relationship between the magnitude of neck-surface vibration (NSVMag; transduced with an accelerometer) and intraoral estimates of subglottal pressure (P'sg) during variations in vocal effort at 3 intensity levels.
Twelve vocally healthy adults produced strings of /pɑ/ syllables in 3 vocal intensity conditions, while increasing vocal effort during each condition. Measures were made of P'sg (estimated during stop-consonant closure), NSVMag (measured during the following vowel), sound pressure level, and respiratory kinematics. Mixed linear regression was used to analyze the relationship between NSVMag and P'sg with respect to total lung volume excursion, levels of lung volume initiation and termination, airflow, laryngeal resistance, and vocal efficiency across intensity conditions.
NSVMag was significantly related to P'sg (p < .001), and there was a significant, although small, interaction between NSVMag and intensity condition. Total lung excursion was the only additional variable contributing to predicting the NSVMag-P'sg relationship.
NSVMag closely reflects P'sg during variations of vocal effort; however, the relationship changes across different intensities in some individuals. Future research should explore additional NSV-based measures (e.g., glottal airflow features) to improve estimation accuracy during voice production.
Relative fundamental frequency (RFF) has shown promise as an acoustic measure of voice, but the subjective and time-consuming nature of its manual estimation has made clinical translation infeasible. Here, a faster, more objective algorithm for RFF estimation is evaluated in a large and diverse sample of individuals with and without voice disorders.
Acoustic recordings were collected from 154 individuals with voice disorders and 36 age- and sex-matched controls with typical voices. These recordings were split into training and 2 testing sets. Using an algorithm tuned to the training set, semi-automated RFF estimates in the testing sets were compared to manual RFF estimates derived from 3 trained technicians.
The semi-automated RFF estimations were highly correlated ( r = 0.82-0.91) with the manual RFF estimates.
Fast and more objective estimation of RFF makes large-scale RFF analysis feasible. This algorithm allows for future work to optimize RFF measures and expand their potential for clinical voice assessment.
Purpose The purpose of this article is to examine the ability of an acoustic measure, relative fundamental frequency (RFF), to distinguish between two subtypes of vocal hyperfunction (VH): phonotraumatic (PVH) and non-phonotraumatic (NPVH).
Method RFF values were compared among control individuals with typical voices (N = 49), individuals with PVH (N = 54), and individuals with NPVH (N = 35).
Results Offset Cycle 10 RFF differed significantly among all 3 groups with values progressively decreasing for controls, individuals with NPVH, and individuals with PVH. Individuals with PVH also had lower Offset Cycles 8 and 9 relative to the other 2 groups and lower RFF values for Offset Cycle 7 relative to controls. There was also a trend for lower Onset Cycle 1 RFF values for the PVH group compared with the NPVH group.
Conclusions RFF values were significantly different between controls and individuals with VH and also between the two subtypes of VH. This study adds further support to the notion that the differences between these two subsets of VH may be functional as well as structural.
This article provides a summary of some recent innovations in voice assessment expected to have an impact in the next 5–10 years on how patients with voice disorders are clinically managed by speech-language pathologists. Specific innovations discussed are in the areas of laryngeal imaging, ambulatory voice monitoring, and “big data” analysis using machine learning to produce new metrics for vocal health. Also discussed is the potential for using voice analysis to detect and monitor other health conditions.
This study analyzes signals recorded using a neck-surface accelerometer from subjects producing speech with different voice modes. The purpose is to explore if the recorded waveforms can capture the glottal vibratory patterns which can be related to the movement of the vocal folds and thus voice quality. The accelerometer waveforms do not contain the supraglottal resonances, and these characteristics make the proposed method suitable for real-life voice quality assessment and monitoring as it does not breach patient privacy. The experiments with a Gaussian mexture model classifier demonstrate that different voice qualities produce distinctly different accelerometer waveforms. The system achieved 80.2% and 89.5% for frame- and utterance-level accuracy, respectively, for classifying among modal, breathy, pressed, and rough voice modes using a speaker-dependent classifier. Finally, the article presents characteristic waveforms for each modality and discusses their attributes.
It has been proven that the improper function of the vocal folds can result in perceptually distorted speech that is typically identified with various speech pathologies or even some neurological diseases. As a consequence, researchers have focused on finding quantitative voice characteristics to objectively assess and automatically detect non-modal voice types. The bulk of the research has focused on classifying the speech modality by using the features extracted from the speech signal. This paper proposes a different approach that focuses on analyzing the signal characteristics of the electroglottogram (EGG) waveform. The core idea is that modal and different kinds of non-modal voice types produce EGG signals that have distinct spectral/cepstral characteristics. As a consequence, they can be distinguished from each other by using standard cepstral-based features and a simple multivariate Gaussian mixture model. The practical usability of this approach has been verified in the task of classifying among modal, breathy, rough, pressed and soft voice types. We have achieved 83% frame-level accuracy and 91% utterance-level accuracy by training a speaker-dependent system.