Voice Type Research Articles

How Does Group Climate Foster or Hinder Employee Voice? A Cross-Level Examination.

Although empirical evidence has accumulated showing that group climate has a significant impact on employee voice, knowledge about how different types of climates may influence voice is limited. Drawing upon the theory of planned behavior, we develop and test a model that explains whether and how the two group climates, cooperation and sanction, differentially predict employee promotive and prohibitive voice. We test the hypotheses using data collected from a sample of 274 full-time employees nested in 58 workgroups across two time periods. The empirical results show that group climate predicts employee voice in different ways: Group cooperation climate has a positive effect on both types of employee voice, whereas group sanction climate shows a negative effect on promotive voice. Individuals’ psychological capital is a cross-level mediator in the relationship between group climate and employee voice. Employees’ prevention focus negatively moderates the relationship between psychological capital and employee voice. These results highlight the important effect of group climate on employee voice in organization and calls on managers to create a favorable environment to increase employees’ psychological capital and voice behaviors.

Enlarging the spectrum of cluster headache: Can voice analysis reveal an extracranial autonomic involvement?

Despite a well-characterized clinical phenotype, cluster headache (CH) is frequently burdened by misdiagnosis or diagnostic delay. In our experience, we noticed a typical voice quality with low and croaking tone in CH patients. We evaluated, by means of digital voice analysis, whether a typical voice quality characterizes CH patients when compared to healthy controls (HC).

Enlarging the spectrum of cluster headache: Extracranial autonomic involvement revealed by voice analysis.

BackgroundPeople with cluster headache (CH) are frequently burdened by misdiagnosis or diagnostic delay. The peculiar somatic and behavioral changes characterizing patients with CH are not useful to improve diagnostic accuracy. In our clinical experience, we noticed a typical voice quality with low and croaking tone in patients with CH.In this cross‐sectional study, we evaluated, by digital voice analysis, whether it is possible to identify typical voice quality characterizing patients with CH compared with healthy controls (HCs). Furthermore, to investigate whether putative differences in voice characteristics could be underpinned by constitutional aspects or pathological processes of vocal cords, subjects underwent a videolaryngostroboscopy. Smoking habits and alcohol consumption were specifically investigated.MethodsAfter conducting digital recording of the voices from both patients with CH and HCs in a soundproof insulated cabin in the laboratory of the Audiology Department, a set of voice parameters was analyzed. We included the measures of fundamental frequency, calculations of jitter and shimmer, and noise‐to‐harmonics ratios as well as quantities related to the spectral tilt (i.e., H1–H2, H1–A1, H1–A2, and H1–A3) in 20 patients with CH and in 13 HCs. A videolaryngostroboscopy was performed in all subjects.ResultsPatients with CH, explored during the cluster bout period, showed significantly lower second harmonic (H1–H2) values compared with HCs (−6.9 ± 7.6 vs. 2.1 ± 6.7, p = 0.002), usually characterizing the so‐called creaky voice. By using a laryngoscopy investigation, a significantly higher prevalence of mild to moderate vocal cord edema and laryngopharyngeal reflux signs were found in patients with CH (100% of patients with CH vs. 15% of HC, p < 0.001).ConclusionCreaky phonation is a “physiological mode of laryngeal operation” usually underpinned by shortened and thickened vocal folds. Creaky voice phonation can be due to a vocal fold's reduced capability to become slack or flaccid secondary to vocal cord edema underpinned by laryngopharyngeal reflux affecting the phonatory mechanisms in patients with CH. The laryngopharyngeal reflux may represent a dysautonomic sign related to the increased parasympathetic tone during in‐bout period, reinforcing the hypothesis of an extracranial autonomic dysfunction as part of CH clinical picture.

Correlations between oropharyngeal and laryngeal anatomy and the frequency of singing voice: A population-based study, in Greece

The aim of this study is to explore if there is correlation between the typical voice classification and oropharyngeal anatomy, using cervical posterior-anterior radiography on professional singers in Epirus, Greece. Methods: 70 professional singers, 35 men and 35 women, were recruited for this study. All participants underwent a cervical posterior-anterior radiographic imaging of their oral pharyngeal and laryngeal area. Results: A statistically significant difference of mean distance was observed for the CI-MHP area (p=0,004), the MHP- SCV area (F=2,62, p=0,032), as well as SCV-AI area (F=11,82, p=0,000). For the average length measured in mm of the phonetic area PA, statistically significant differences were computed among all the singers in the group (F [5] = 5.368, p = 0.001), as well as the OPC area (F = 6,48, p = 0,000). Conclusions: The cervical posteroanterior radiography provided new correlations of the voice category of professional singers with their Oropharyngeal and Laryngeal Anatomy.

Adding LCMPT Terms to Local Catalog Records for Notated Music for Voice and Piano

ABSTRACT Medium of performance is a valuable way to find music materials, but it is challenging to search by medium in library catalogs. Songs for specific voice types are especially difficult to look for using legacy metadata such as subject headings. This article describes a project to add terms from Library of Congress Medium of Performance Thesaurus for Music to records for scores for songs with piano in an academic library catalog. Appropriate terms are identified by examining subject headings and uncontrolled fields in the records. Adding these terms can enhance discovery and enables deeper analysis of the collection.

Business elites and populism: understanding business responses

ABSTRACT This article analyses the challenges to business associated with the rise of right-wing populism. It discusses how the populist turn has weakened key underpinnings of business power in the neoliberal era, notably the prevalence of quiet politics and network governance. This article analyses the complex strategic dilemmas these challenges create for business. Extending Hirschman’s classic model of exit, voice and loyalty, it develops a novel typology of five business responses to the rise of populism: exit, soft voice, loud voice, explicit loyalty and implicit loyalty. The extended typology highlights two key dilemmas related to the appropriate degree of engagement with politics which business faces in the populist era and which are reflected in the contrast between two types of voice and loyalty. The article analyses the factors shaping which strategy businesses are likely to choose and reflects on the broader implications of this analysis for state-business relations and business power.

Public Perceptions Towards Synthetic Voice Technology

Text-to-Speech (TTS) technologies have provided ways to produce acoustic approximations of human voices. However, recent advancements in machine learning (i.e., neural network TTS) have helped move beyond coarse mimicry and towards more natural-sounding speech. With only a small collection of recorded utterances, it is now possible to generate wholly synthetic voices indistinguishable from those of human speakers. While these new approaches to speech synthesis can help facilitate more seamless experiences with artificial agents, they also lower the barrier to entry for those seeking to perpetrate deception. As such, in the development of these technologies, it is important to anticipate potential harms and devise strategies to help mitigate against misuse. This paper presents findings from a 360-person survey that assessed public perceptions of synthetic voices, with a particular focus on how voice type and social scenarios impact ratings of trust. Findings have implications for the responsible deployment of synthetic speech technologies.

Classification of Women's Voices Using Fast Fourier Transform (FFT) Method

Everyone has a different kind of voice. Based on gender, voice type is divided into six parts, namely soprano, mezzo soprano, and alto for women; and tenor, baritone, and bass in men. Each type of sound has a different range and with different frequencies. This study classified the type of voice in women using the Fast Fourier Transform (FFT) method by recording the voices of each user which would then be processed using the FFT method to obtain the appropriate sound range. This research got results with an accuracy of up to 80%.The results obtained from this study are quite appropriate and it is proven that the FFT method can be used in digital signal processing.

Voice and Insomnia: A Daily Study of Underlying Affective and Cognitive Mechanisms

In this study, we examined whether the expression of voice, as a prosocial yet psychologically costly behavior, during the day affects employee sleep quality at night and whether sleep quality affects the expression of voice on the next workday. Having surveyed 113 full-time employees twice a day over the course of two weeks, we found that the same-day effect of voice on sleep and the effect of sleep on the next day voice depend on the type of voice. Specifically, we found that employees who express promotive voice experience higher levels of positive affect at the end of the workday, more effectively detach from work at home, and as a result are less likely to suffer from insomnia at night. We also found that employees who express prohibitive voice tend to experience higher levels of negative affect at the end of the workday, are unable to effectively detach from work at home, and consequently are more likely to experience insomnia at night. Our study further demonstrates that, while there is no association between insomnia and next-day prohibitive voice, sleep-deprived employees are less likely to engage in promotive voice on the next day because of being psychologically depleted.

Songs for Lyric Tenor: An Analysis of Five Francesco Paolo Tosti Songs

Selecting diverse repertoire for the undergraduate lyric tenor can be a challenging process for instructors of this voice type. Francesco Paolo Tosti, a lyric tenor and composer, wrote songs that exemplify technical concepts that should be cultivated in the undergraduate lyric tenor voice. This guide will present five Tosti songs that address technical issues, which the lyric tenor must overcome to progress into more advanced repertoire.

Blessing or curse: The moderating role of political skill in the relationship between servant leadership, voice, and voice endorsement

SummaryDrawing on social exchange theory and social‐political theory of influence, we hypothesize that followers reciprocate servant leadership by engaging in voice behavior. Further, we propose that promotive and prohibitive forms of voice are differentially endorsed by leaders and that follower political skill is pivotal in the social influence process of converting the two types of voice to managerial endorsement. To test our hypothesized model, we collected three‐wave, multi‐source data from 308 architects and their leaders from a large architecture firm in China. Our results confirmed that servant leadership was positively related to both follower promotive and prohibitive voice. Moreover, regarding the moderating role of follower political skill, we found that the relationship between servant leadership, promotive voice, and voice endorsement was positive when follower political skill was high, while it was not significant for followers with low political skill. In contrast, the relationship between servant leadership, prohibitive voice, and voice endorsement was positive under the low political skill condition, while it was negative when follower political skill was high. Our results suggest a divergent role of political skill in converting different types of voice to managerial endorsement.

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

Effects of Sidetone Amplification on Vocal Function During Telecommunication

Society has become increasingly dependent on telecommunication, which has been shown to negatively impact vocal function. This study explores the use of sidetone regulation during audio-visual communication as one potential technique to alleviate the effects of telecommunication on the voice. The speech acoustics of 18 participants with typical voices were measured during conversational tasks during three conditions of sidetone amplification: baseline (no sidetone amplification), low sidetone amplification, and high sidetone amplification. Vocal intensity, vocal quality (estimated using acoustic measures of the low-high ratio and the smoothed cepstral peak prominence), and self-perceived vocal effort were used to measure the impacts of sidetone amplification on vocal function. Compared to baseline, there were statistically significant decreases in vocal intensity and increases in low-high ratio in the high level of sidetone amplification condition. Changes in these measures were not significantly correlated. When asked to rank conditions based on their perceived vocal effort, participants most often ranked the high level of sidetone amplification as least effortful; however, the visual-analog ratings of vocal effort were not significantly different between conditions. The smoothed cepstral peak prominence did not change with varying levels of sidetone amplification. Vocal intensity decreased with high levels of sidetone amplification. High levels of sidetone amplification also resulted in increases in the low-high ratio, which were shown to be more than just a byproduct of decreased vocal intensity. The impact of sidetone amplification on vocal effort was less clear, but results suggested that participants generally decreased their vocal effort with increased levels of sidetone amplification. This was a preliminary study and future work is warranted in a population of participants with voice complaints and in a more noisy, realistic environments.

Future Forecasting of COVID-19: A Supervised Learning Approach.

A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.

Library Audiobook System Using Speech Recognition

Developments in man-made consciousness are beginning to be used successfully in human life, enabled by the use of things in different ways. Independent devices are becoming more cunning in their way of communicating in modern ways by using new technologies. The invention of perceiving modern technology to a person is one of the useful trends. For voice recognition, this project presents an exceptionally on-demand, fast, and user-friendly Python using Visual Studio Code. This software helps transform text to speech and vice versa. Voice or some other type of voice can be converted into the text instead of tapping on a keyboard. Any kind of voice message can be stated in this app as the input, and the corresponding text will appear on the screen. Modern technology can easily generate text. The recognized text can be stored in a file with GUI/Voice command. This application useful for people with physical disabilities like deaf or handicapped users who are often difficult, painful, or impossible to type, and also for people who can understand while others speaking but cannot read the words.

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures.

Methods for automating relative fundamental frequency (RFF)—an acoustic estimate of laryngeal tension—rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced–unvoiced–voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm (“aRFF-APH”). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm (“aRFF-AP”), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.

Evaluating the Voice Type Component Distributions of Excised Larynx Phonations at Three Subglottal Pressures.

Purpose The excised canine larynx provides an advantageous experimental framework in the study of voice physiology. In recent years, signal processing methods have been applied to analyze phonations in excised canine larynx experiments. However, phonations have a highly complex and nonstationary nature corresponding to different proportions of regular and chaotic signal elements. Current nonlinear dynamic methods that are used to assess the degree of irregularity in the voice fail to recognize the distribution of voice type components (VTCs). Method Based on measures of intrinsic dimension, this article presents a method to analyze the VTC distribution of phonations in excised canine larynx experiments. Thirty-nine phonation samples from 13 excised canine larynges at three different subglottal pressures were analyzed. Results Phonation produced with subglottal pressures above phonation instability pressure (PIP) and below phonation threshold pressure (PTP) resulted in high proportions of Voice Types 3 and 4, characterized by chaotic and noisy signals. Phonation produced with pressure between PTP and PIP contained mostly Type 1 voice, characterized by a regular and nearly periodic signal. Mean proportions of all VTCs varied significantly in comparisons of phonations produced with Sub-PTP and PTP as well as in comparisons of phonations produced with PTP and PIP. Conclusions Across all VTCs, the VTC profiles of normal and abnormal phonation differ significantly. Normal phonation is strongly associated with VTC1 (Voice Type Component 1), whereas abnormal phonation exhibits increased VTC4 (Voice Type Component 4). The study further demonstrates the ability of intrinsic dimension to successfully detect multiple voice types in an acoustic signal and highlights the need for expanded use of intrinsic dimension in human voice. Supplemental Material https://doi.org/10.23641/asha.14417585.

Effect of Voice Type and Head-Light Color in Social Robots for Different Applications

The social robotics field has been growing in prominence. Many developed countries have begun to apply such robots in various fields, such as shopping reception, education, home companion, medical care, and security. However, expectations of social robots have also changed. In addition to performing their specific tasks, social robots are also expected to consider users’ emotional needs. Therefore, scholars have begun to explore robots’ social cues in an effort to enhance their role in society and enable them to provide higher-quality services. This study used the robot's voice type, head-light color, and application field as independent variables to discuss the optimal robot social cues for different applications and to provide a reference for future production and research. Education, shopping reception, and home companion applications were selected as the most common areas employing social robots. According to the vocal fundamental frequencies used in related studies, three voice types were included: male, female, and child. The three values for head-light color, namely warm, neutral, and cold colors, were set according to color temperature theory. The robot social attribute scale, with the addition of an acceptance component, was used to evaluate respondent perceptions. Results revealed that male voices provide users with the highest impression of competence, whereas children's voices have the lowest competence. However, results for the warmth component were completely different: in this aspect, children's voices had the highest evaluations and male voices the lowest. For head-light colors, neutral colors had the highest overall acceptance, the highest competence evaluation, and the lowest discomfort. The neutral color's warmth judgment was the same as that of the warm color, and its competence rating was the same as that of the cold color. For home companion robots, we recommend a child’s voice with neutral colors as the first choice, and a child’s voice with warm color as the second option. Because females rejected male voices, male voices are not recommended in home companion. For education and business, a male voice and neutral colors are the first choice. A female voice is also the second option. In cases with less focus on warmth impression but greater emphasis on competence impression, cold colors could be employed.

The sounds of safety silence: Interventions and temporal patterns unmute unique safety voice content in speech

Research shows that withholding safety concerns on encountering hazards – safety silence– is a critical contributor to accidents. Studies therefore aim to prevent accidental harm through interventions for reducing safety silence. Yet, the behaviour remains poorly understood, obstructing effective safety management: it is unclear to what extent safety silence involves muted safety voice (the partial withholding of safety concerns), and how muted safety voice can be recognised in speech, may be measured based on the degrees and types of safety voice (speaking up about safety), progresses over time, and may be optimally reduced. To improve safety management, this study proposes a conceptual model for the manifestation of safety silence and muted safety voice using a laboratory experiment (N = 404) to evaluate the implications for the effectiveness of three interventions (salient hazards, clear responsibilities, encouragements) across stages of a hazard. Results indicated that safety silence and muted safety voice are measurable in terms of the degree to which concerned people engage in five types of safety voice at different points in time, and we revealed this is important for safety management: interventions only unmute safety voice at unique hazard stages and for knowledge-based speech when people are concerned. This indicates that safety silence and muted safety voice are situated and can be recognised in nuanced speech, with interventions being most effective when timed appropriately and people have safety concerns to speak up about.

Generating Robotic Speech Prosody for Human Robot Interaction: A Preliminary Study

The use of affective speech in robotic applications has increased in recent years, especially regarding the developments or studies of emotional prosody for a specific group of people. The current work proposes a prosody-based communication system that considers the limited parameters found in speech recognition for the elderly, for example. This work explored what types of voices were more effective for understanding presented information, and if the affects of robot voices reflected on the emotional states of listeners. By using functions of a small humanoid robot, two different experiments conducted to find out comprehension level and the affective reflection respectively. University students participated in both tests. The results showed that affective voices helped the users understand the information, as well as that they felt corresponding negative emotions in conversations with negative voices.