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Relevant bibliographies by topics / Voice quality

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Academic literature on the topic 'Voice quality'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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Journal articles on the topic "Voice quality"

1

Kreiman, Jody, Bruce R. Gerratt, Kristin Precoda, and Gerald S. Berke. "Individual Differences in Voice Quality Perception." Journal of Speech, Language, and Hearing Research 35, no. 3 (June 1992): 512–20. http://dx.doi.org/10.1044/jshr.3503.512.

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Sixteen listeners (10 expert, 6 naive) judged the dissimilarity of pairs of voices drawn from pathological and normal populations. Separate nonmetric multidimensional scaling solutions were calculated for each listener and voice set. The correlations between individual listeners’ dissimilarity ratings were low However, scaling solutions indicated that each subject judged the voices in a reliable, meaningful way. Listeners differed more from one another in their judgments of the pathological voices (which varied widely on a number of acoustic parameters) than they did for the normal voices (which formed a much more homogeneous set acoustically). The acoustic features listeners used to judge dissimilarity were predictable from the characteristics of the stimulus sets’ only parameters that showed substantial variability were perceptually salient across listeners. These results are consistent with prototype models of voice perception They suggest that traditional means of assessing listener reliability n voice perception tasks may not be appropriate, and highlight the importance of using explicit comparisons between stimuli when studying voice quality perception

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Kreiman, Jody. "Information conveyed by voice quality." Journal of the Acoustical Society of America 155, no. 2 (February 1, 2024): 1264–71. http://dx.doi.org/10.1121/10.0024609.

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The problem of characterizing voice quality has long caused debate and frustration. The richness of the available descriptive vocabulary is overwhelming, but the density and complexity of the information voices convey lead some to conclude that language can never adequately specify what we hear. Others argue that terminology lacks an empirical basis, so that language-based scales are inadequate a priori. Efforts to provide meaningful instrumental characterizations have also had limited success. Such measures may capture sound patterns but cannot at present explain what characteristics, intentions, or identity listeners attribute to the speaker based on those patterns. However, some terms continually reappear across studies. These terms align with acoustic dimensions accounting for variance across speakers and languages and correlate with size and arousal across species. This suggests that labels for quality rest on a bedrock of biology: We have evolved to perceive voices in terms of size/arousal, and these factors structure both voice acoustics and descriptive language. Such linkages could help integrate studies of signals and their meaning, producing a truly interdisciplinary approach to the study of voice.

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Kreiman, Jody, Bruce R. Gerratt, and Kristin Precoda. "Listener Experience and Perception of Voice Quality." Journal of Speech, Language, and Hearing Research 33, no. 1 (March 1990): 103–15. http://dx.doi.org/10.1044/jshr.3301.103.

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Five speech-language clinicians and 5 naive listeners rated the similarity of pairs of normal and dysphonic voices. Multidimensional scaling was used to determine the voice characteristics that were perceptually important for each voice set and listener group. Solution spaces were compared to determine if clinical experience affects perceptual strategies. Naive and expert listeners attended to different aspects of voice quality when judging the similarity of voices, for both normal and pathological voices. All naive listeners used similar perceptual strategies; however, individual clinicians differed substantially in the parameters they considered important when judging similarity. These differences were large enough to suggest that care must be taken when using data averaged across clinicians, because averaging obscures important aspects of an individual’s perceptual behavior.

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Kreiman, Jody, Bruce R. Gerratt, Gail B. Kempster, Andrew Erman, and Gerald S. Berke. "Perceptual Evaluation of Voice Quality." Journal of Speech, Language, and Hearing Research 36, no. 1 (February 1993): 21–40. http://dx.doi.org/10.1044/jshr.3601.21.

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The reliability of listeners’ ratings of voice quality is a central issue in voice research because of the clinical primacy of such ratings and because they are the standard against which other measures are evaluated. However, an extensive literature review indicates that both intrarater and interrater reliability fluctuate greatly from study to study. Further, our own data indicate that ratings of vocal roughness vary widely across individual clinicians, with a single voice often receiving nearly the full range of possible ratings. No model or theoretical framework currently exists to explain these variations, although such a model might guide development of efficient, valid, and standardized clinical protocols for voice evaluation. We propose a theoretical framework that attributes variability in ratings to several sources (including listeners’ backgrounds and biases, the task used to gather ratings, interactions between listeners and tasks, and random error). This framework may guide development of new clinical voice and speech evaluation protocols, ultimately leading to more reliable perceptual ratings and a better understanding of the perceptual qualities of pathological voices.

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Pessoa, Aline Neves, Beatriz Cavalcanti de Albuquerque Caiuby Novaes, Lilian Kuhn Pereira, and Zuleica Antonia Camargo. "Voice quality and voice dynamics data." Journal of Speech Sciences 1, no. 2 (February 3, 2021): 17–33. http://dx.doi.org/10.20396/joss.v1i2.15024.

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Acoustic and perceptual auditory analysis procedures present themselves as clinical tools which give support to the understanding of the speech features of hearing impaired children (HIC). Voice quality stems from the overlapped action of the larynx, the supralaryngeal vocal tract and the level of muscular tension throughout the speech flow. Nonetheless, voice dynamics is characterized by frequency, duration and intensity variations. This research aimed at investigating acoustic and perceptive correlates of a HIC child’s voice and dynamic quality. The child, who has a cochlear implanted, had his speech samples collected during speech therapy sessions. The male subject (R), who uses a unilateral cochlear implant (UCI), had his speech production samples recorded when he was 5 (05 samples) and 6 years old (05 samples), and which were later tagged Cut A and Cut B respectively. The recorded corpus was acoustically analyzed through the use of the SGEXpressionEvaluator script (Barbosa, 2009) running on the free software Praat v10. The measures which were automatically extracted by the script correspond to the fundamental frequency –f0, first f0 derivative, intensity, spectral fall and long term spectrum. The perceptual auditory analysis of the voice quality was based on the VPAS-PB script (Camargo e Madureira, 2008). The perceptual auditory judgments and the acoustic measures were subjected to statistical analysis procedures. At first the, the data (perceptual and acoustic) were separately analyzed through a hierarchical and agglomerative cluster analysis. Subsequently, they were examined together through the principal component analysis. Results revealed the existence of correspondence between the acoustic and perceptual auditory data. In the audio recorded data samples from Cut B (one year after the first one) greater variability tendencies in acoustic measures of f0 could be observed associated with laryngeal hyper function at the perceptual level plus silent pauses and the reduction of speech rate. From the integrated acoustic and perceptual analysis it was possible to keep a record of the child’s oral language development process. The data analysis in this study allowed the observation of several interaction levels between the vocal tract (lip movement extension adjusts, tongue and jaw, associated with velopharyngeal adjusts and muscular tension from the larynx), plus the inspection of speech dynamics elements (habitual pitch and speech rate) of a child’s speech who has a UCI implanted during a one-year-speech-therapy-process period. This source of information made the characterization of the child’s evolution possible, especially in terms of perceptual auditory analysis descriptions being phonetically motivated by the speech dynamics quality.

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Cuciniello, Marialucia, Terry Amorese, Claudia Greco, Zoraida Callejas Carrión, Carl Vogel, Gennaro Cordasco, and Anna Esposito. "A Synthetic Voice for an Assistive Conversational Agent: A Survey to Discover Italian Preferences regarding Synthetic Voice’s Gender and Quality Level." Human Behavior and Emerging Technologies 2023 (December 28, 2023): 1–15. http://dx.doi.org/10.1155/2023/8858268.

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Based on a previous investigation, a quantitative study aimed to identify user’ preferences towards four synthetic voices of two different quality levels (classified through the sophistication of the synthesizer: low vs. high) is proposed. The voices administered to participants were developed considering two main aspects: the voice quality (high/low) and their gender (male/female). 182 unpaid participants were recruited for the study, divided in four groups according to their age, and therefore classified as adolescents, young adults, middle-aged, and seniors. To collect data regarding each voice, randomly audited by participants, the shortened version of the Virtual Agent Voice Acceptance Questionnaire (VAVAQ) was exploited. Outcomes of the previous study revealed that the voices of high quality, regardless of their gender, received a higher acclaim by all participants examined rather than the corresponding two voices assessed as lower quality. Conversely, findings of the current study suggest that the four new groups of participants involved agreed in showing their strong preference towards the high-quality voice gendered as female compared to all the other considered voices. Regarding the two voices gendered as male, the high-quality one was considered as more original and capable to arouse positive emotional states than the low-quality one. Moreover, the high-quality male voice was judged as more natural than the female low-quality one. Results provide some insights for future directions in the user experience and design field.

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Fujimura, Osamu, Kiyoshi Honda, Hideki Kawahara, Yasuyuki Konparu, Masanori Morise, and J. C. Williams. "Noh voice quality." Logopedics Phoniatrics Vocology 34, no. 4 (January 2009): 157–70. http://dx.doi.org/10.3109/14015430903002288.

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Zhang, Yu, Rongjie Huang, Ruiqi Li, JinZheng He, Yan Xia, Feiyang Chen, Xinyu Duan, Baoxing Huai, and Zhou Zhao. "StyleSinger: Style Transfer for Out-of-Domain Singing Voice Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19597–605. http://dx.doi.org/10.1609/aaai.v38i17.29932.

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Style transfer for out-of-domain (OOD) singing voice synthesis (SVS) focuses on generating high-quality singing voices with unseen styles (such as timbre, emotion, pronunciation, and articulation skills) derived from reference singing voice samples. However, the endeavor to model the intricate nuances of singing voice styles is an arduous task, as singing voices possess a remarkable degree of expressiveness. Moreover, existing SVS methods encounter a decline in the quality of synthesized singing voices in OOD scenarios, as they rest upon the assumption that the target vocal attributes are discernible during the training phase. To overcome these challenges, we propose StyleSinger, the first singing voice synthesis model for zero-shot style transfer of out-of-domain reference singing voice samples. StyleSinger incorporates two critical approaches for enhanced effectiveness: 1) the Residual Style Adaptor (RSA) which employs a residual quantization module to capture diverse style characteristics in singing voices, and 2) the Uncertainty Modeling Layer Normalization (UMLN) to perturb the style attributes within the content representation during the training phase and thus improve the model generalization. Our extensive evaluations in zero-shot style transfer undeniably establish that StyleSinger outperforms baseline models in both audio quality and similarity to the reference singing voice samples. Access to singing voice samples can be found at https://stylesinger.github.io/.

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Eskenazi, L., D. G. Childers, and D. M. Hicks. "Acoustic Correlates of Vocal Quality." Journal of Speech, Language, and Hearing Research 33, no. 2 (June 1990): 298–306. http://dx.doi.org/10.1044/jshr.3302.298.

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We have investigated the relationship between various voice qualities and several acoustic measures made from the vowel /i/ phonated by subjects with normal voices and patients with vocal disorders. Among the patients (pathological voices), five qualities were investigated: overall severity, hoarseness, breathiness, roughness, and vocal fry. Six acoustic measures were examined. With one exception, all measures were extracted from the residue signal obtained by inverse filtering the speech signal using the linear predictive coding (LPC) technique. A formal listening test was implemented to rate each pathological voice for each vocal quality. A formal listening test also rated overall excellence of the normal voices. A scale of 1–7 was used. Multiple linear regression analysis between the results of the listening test and the various acoustic measures was used with the prediction sums of squares (PRESS) as the selection criteria. Useful prediction equations of order two or less were obtained relating certain acoustic measures and the ratings of pathological voices for each of the five qualities. The two most useful parameters for predicting vocal quality were the Pitch Amplitude (PA) and the Harmonics-to-Noise Ratio (HNR). No acoustic measure could rank the normal voices.

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Delgado Hernández, Jonathan, Nieves M. León Gómez, Alejandra Jiménez, Laura M. Izquierdo, and Ben Barsties v. Latoszek. "Validation of the Acoustic Voice Quality Index Version 03.01 and the Acoustic Breathiness Index in the Spanish language." Annals of Otology, Rhinology & Laryngology 127, no. 5 (February 28, 2018): 317–26. http://dx.doi.org/10.1177/0003489418761096.

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Objective: The aim of this study was to validate the Acoustic Voice Quality Index 03.01 (AVQIv3) and the Acoustic Breathiness Index (ABI) in the Spanish language. Method: Concatenated voice samples of continuous speech (cs) and sustained vowel (sv) from 136 subjects with dysphonia and 47 vocally healthy subjects were perceptually judged for overall voice quality and breathiness severity. First, to reach a higher level of ecological validity, the proportions of cs and sv were equalized regarding the time length of 3 seconds sv part and voiced cs part, respectively. Second, concurrent validity and diagnostic accuracy were verified. Results: A moderate reliability of overall voice quality and breathiness severity from 5 experts was used. It was found that 33 syllables as standardization of the cs part, which represents 3 seconds of voiced cs, allows the equalization of both speech tasks. A strong correlation was revealed between AVQIv3 and overall voice quality and ABI and perceived breathiness severity. Additionally, the best diagnostic outcome was identified at a threshold of 2.28 and 3.40 for AVQIv3 and ABI, respectively. Conclusions: The AVQIv3 and ABI showed in the Spanish language valid and robust results to quantify abnormal voice qualities regarding overall voice quality and breathiness severity.

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Dissertations / Theses on the topic "Voice quality"

1

Beck, J. M. "Organic variation and voice quality." Thesis, University of Edinburgh, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382921.

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Laver, J. D. M. H. "Individual features in voice quality." Thesis, University of Edinburgh, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376885.

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Tasyumruk, Lutfullah. "Analysis of voice quality problems of Voice Over Internet Protocol (VoIP)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03sep%5FTasyumruk.pdf.

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Ho, Yuen-yan Eva. "Voice quality change using humming technique." Click to view the E-thesis via HKUTO, 1999. http://sunzi.lib.hku.hk/hkuto/record/B36209892.

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Thesis (B.Sc)--University of Hong Kong, 1999.
"A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, May 14, 1999." Also available in print.

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Jalalinajafabadi, Farideh. "Computerised GRBAS assessement of voice quality." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/computerised-grbas-assessement-of-voice-quality(7efd3263-b109-4137-87cf-b9559c61730b).html.

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Vocal cord vibration is the source of voiced phonemes in speech. Voice quality depends on the nature of this vibration. Vocal cords can be damaged by infection, neck or chest injury, tumours and more serious diseases such as laryngeal cancer. This kind of physical damage can cause loss of voice quality. To support the diagnosis of such conditions and also to monitor the effect of any treatment, voice quality assessment is required. Traditionally, this is done ‘subjectively’ by Speech and Language Therapists (SLTs) who, in Europe, use a well-known assessment approach called ‘GRBAS’. GRBAS is an acronym for a five dimensional scale of measurements of voice properties. The scale was originally devised and recommended by the Japanese Society of Logopeadics and Phoniatrics and several European research publications. The proper- ties are ‘Grade’, ‘Roughness’, ‘Breathiness’, ‘Asthenia’ and ‘Strain’. An SLT listens to and assesses a person’s voice while the person performs specific vocal maneuvers. The SLT is then required to record a discrete score for the voice quality in range of 0 to 3 for each GRBAS component. In requiring the services of trained SLTs, this subjective assessment makes the traditional GRBAS procedure expensive and time-consuming to administer. This thesis considers the possibility of using computer programs to perform objective assessments of voice quality conforming to the GRBAS scale. To do this, Digital Signal Processing (DSP) algorithms are required for measuring voice features that may indicate voice abnormality. The computer must be trained to convert DSP measurements to GRBAS scores and a ‘machine learning’ approach has been adopted to achieve this. This research was made possible by the development, by Manchester Royal Infirmary (MRI) Hospital Trust, of a ‘speech database’ with the participation of clinicians, SLT’s, patients and controls. The participation of five SLTs scorers allowed norms to be established for GRBAS scoring which provided ‘reference’ data for the machine learning approach. To support the scoring procedure carried out at MRI, a software package, referred to as GRBAS Presentation and Scoring Package (GPSP), was developed for presenting voice recordings to each of the SLTs and recording their GRBAS scores. A means of assessing intra-scorer consistency was devised and built into this system. Also, the assessment of inter-scorer consistency was advanced by the invention of a new form of the ‘Fleiss Kappa’ which is applicable to ordinal as well as categorical scoring. The means of taking these assessments of scorer consistency into account when producing ‘reference’ GRBAS scores are presented in this thesis. Such reference scores are required for training the machine learning algorithms. The DSP algorithms required for feature measurements are generally well known and available as published or commercial software packages. However, an appraisal of these algorithms and the development of some DSP ‘thesis software’ was found to be necessary. Two ‘machine learning’ regression models have been developed for map- ping the measured voice features to GRBAS scores. These are K Nearest Neighbor Regression (KNNR) and Multiple Linear Regression (MLR). Our research is based on sets of features, sets of data and prediction models that are different from the approaches in the current literature. The performance of the computerised system is evaluated against reference scores using a Normalised Root Mean Squared Error (NRMSE) measure. The performances of MLR and KNNR for objective prediction of GRBAS scores are compared and analysed ‘with feature selection’ and ‘without feature selection’. It was found that MLR with feature selection was better than MLR without feature selection and KNNR with and without feature selection, for all five GRBAS components. It was also found that MLR with feature selection gives scores for ‘Asthenia’ and ‘Strain’ which are closer to the reference scores than the scores given by all five individual SLT scorers. The best objective score for ‘Roughness’ was closer than the scores given by two SLTs, roughly equal to the score of one SLT and worse than the other two SLT scores. The best objective scores for ‘Breathiness’ and ‘Grade’ were further from the reference scores than the scores produced by all five SLT scorers. However, the worst ‘MLR with feature selection’ result has normalised RMS error which is only about 3% worse than the worst SLT scoring. The results obtained indicate that objective GRBAS measurements have the potential for further development towards a commercial product that may at least be useful in augmenting the subjective assessments of SLT scorers.

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Ma, Pui-man Estella. "Assessing voice activity and participation implication of clinical management in voice disorders /." Click to view the E-thesis via HKUTO, 1999. http://sunzi.lib.hku.hk/hkuto/record/B36210031.

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Thesis (B.Sc)--University of Hong Kong, 1999.
"A dissertation submitted in partial fulfilment of the requirements for the Bachelor of Science (Speech and Hearing Sciences), The University of Hong Kong, April 30, 1999." Also available in print.

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Anskaitis, Aurimas. "Analysis of Quality of Coded Voice Signals." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2009~D_20100303_142141-66509.

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The dissertation investigates the problem of quality of coded voice. The main attention is paid to voice quality evaluation under packet loss conditions. The aim of the work is to improve voice quality evaluation algorithms. The tasks of the work are: • construction of the means for measurement of voice quality of short voice signals; • to define the concept of value of coded voice segment and to choose corresponding value metrics; • to measure distributions of frame values in standard voice; • to establish limits of distortions created by different codecs; • to investigate inertia of wide spread codecs and establish the length of impact of one lost frame. The dissertation consists of the introduction, 4 chapters, conclusions, list of literature. Introduction presents the novelty and topicality of the work, tasks and aims of the work are formulated. The first chapter is overview of voice quality evaluation methods, pros and cons of these methods are analyzed. PESQ algorithm and limits of its applicability are introduced in this chapter too. The lists of Lithuanian words for word intelligibility testing are created. Chapter two presents the method of signal construction that allows to extend PESQ applicability to short signals. This chapter introduces the concept of frame value. Distributions of frame values are calculated. Third chapter analyses distortions created by coding. It is shown that coding distortions... [to full text]
Disertacijoje nagrin jama koduoto balso kokybės vertinimo problematika. Pagrindinis dėmesys skiriamas balso kokybės tyrimams, kai perduodama koduota šneka ir prarandami balso paketai. Darbo tikslas yra patobulinti koduoto balso kokybės vertinimo algoritmus. Darbo uždaviniai yra šie: • sukurti matavimo priemonę trumpų balso signalo atkarpų kokybei vertinti; • apibrėžti koduoto balso segmentų vertės sampratą ir parinkti vertės metrikas; • išmatuoti bendrinės šnekos balso segmentų verčių skirstinius; • nustatyti skirtingų koderių sukuriamų iškraipymų ribas; • ištirti paplitusių koderių inertiškumą, nustatyti kiek laiko pastebima prarastų paketų įtaka sekantiems segmentams. Disertaciją sudaro įvadas, keturi tiriamieji skyriai ir bendrosios išvados. Įvade pristatomas darbo naujumas, aktualumas, aptariamas autoriaus indėlis, formuluojami darbo tikslai. Pirmas skyrius yra apžvalginis – analizuojami balso kokybės vertinimo metodai, jų privalumai ir trūkumai. Kaip savarankiška dalis čia pristatyti autoriaus sudaryti sąrašai lietuviškų žodžių, skirtų šnekos suprantamumo tyrimams. Antrame skyriuje parodoma, kaip galima išplėsti kokybės vertinimo PESQ (angl. Perceptual Evaluation of Speech Quality) algoritmo taikymo ribas. Čia įvedama koduoto balso paketo vertės sąvoka, nustatomi statistiniai paketų vertės skirstiniai. Trečiame skyriuje nagrinėjami specifiniai koduotos šnekos iškraipymai ir kodavimo parametrų įtaka... [toliau žr. visą tekstą]

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Manka, David L. "Voice over Internet Protocol testbed design for non-intrusive, objective voice quality assessment." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FManka.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2007.
Thesis Advisor(s): Tummala, Murali ; McEachen, John C. "September 2007." Description based on title screen as viewed on October 23, 2007. Includes bibliographical references (p.91-94). Also available in print.

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Ast, Jered Daniel Pendse Ravindra. "The effect of dynamic voice codec selection for active calls on voice quality." Diss., A link to full text of this thesis in SOAR, 2007. http://soar.wichita.edu/dspace/handle/10057/1114.

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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering.
"May 2007." Title from PDF title page (viewed on December 14, 2007). Thesis adviser: Ravi Pendse. Includes bibliographic references (leaves 67-69).

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Kramer, Elena [Verfasser]. "Predicting perceptual voice quality from objective voice parameters in dysphonic patients / Elena Kramer." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2013. http://d-nb.info/1029994641/34.

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Books on the topic "Voice quality"

1

Banerjie, Maumita. Voice culture: The art of developing the quality of voice. Ghaziabad: Naitik Prakashan, 2019.

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1927-, Fujimura Osamu, Hirano Minoru 1932-, and Vocal Fold Physiology Conference (8th : 1994 : Kurume-shi, Japan), eds. Vocal fold physiology: Voice quality control. San Diego, Calif: Singular Pub. Group, 1995.

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Redding, Christopher. Voice quality assessment of vocoders in tandem configuration. [Washington, D.C.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2001.

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N, DeMinco, Lindner Jeanne, United States. National Telecommunications and Information Administration, and Institute for Telecommunication Sciences, eds. Voice quality assessment of vocoders in tandem configuration. [Washington, D.C.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2001.

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Burchill, Gary. Voices into choices: Acting on the voice of the customer. Madison, WI: Joiner Publication, 1997.

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Gilman, Marina. Body and voice: Somatic re-education. San Diego, CA: Plural Publishing, Inc., 2014.

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Krzysztof, Izdebski, ed. Emotions in the human voice. San Diego, CA: Plural Pub., 2008.

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Verma, Pramode K. Voice over IP Networks: Quality of Service, Pricing and Security. Berlin, Heidelberg: Springer -Verlag Berlin Heidelberg, 2011.

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Mohammad, Abu Z., and Rahman Md Mostafizur. Report card survey 2015: Citizen's voice for quality primary education. Edited by Campaign for Popular Education (Organization). Dhaka: Campaign for Popular Education, 2015.

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Vurma, Allan. Voice quality and pitch in singing: Some aspects of perception and production = Häälekvaliteet ja helikõrgus laulmisel : mõningad taju ja moodustusega seotud aspektid. Tallinn: Estonian Academy of Music and Theater, Department of Musicology, 2007.

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Book chapters on the topic "Voice quality"

1

Thomas, Erik R. "Voice Quality." In Sociophonetics, 224–50. London: Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-28561-4_7.

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Reed, Beatrice Szczepek. "Voice Quality." In Analysing Conversation, 199–218. London: Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-04514-0_10.

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van Leeuwen, Theo. "Voice quality and timbre." In Speech, Music, Sound, 125–55. London: Macmillan Education UK, 1999. http://dx.doi.org/10.1007/978-1-349-27700-1_6.

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Möller, Sebastian. "Quality of Voice and Audio Transmission Systems." In Quality Engineering, 73–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65615-0_5.

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Inoguchi, Takashi. "Exit, Voice, and Loyalty." In Quality of Life in Asia, 5–6. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4724-4_2.

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Bunch, Meribeth A. "Resonation and vocal quality." In Dynamics of the Singing Voice, 82–110. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-2065-1_5.

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Bunch, Meribeth A. "Resonation and vocal quality." In Dynamics of the Singing Voice, 82–110. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-3691-1_5.

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Marsden, Paul. "Wearable and voice-controlled technology." In Digital Quality Management in Construction, 165–71. Title: Digital quality management in construction/Paul Marsden. Description: Abingdon, Oxon; New York, NY: Routledge is an imprint of the Taylor & Francis Group, an Informa Business, 2019.: Routledge, 2019. http://dx.doi.org/10.1201/9780429423062-16.

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Brown, Adam. "English With My Native Voice Quality, My Native Language With English Voice Quality." In Activities and Exercises for Teaching English Pronunciation, 48–52. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003092247-10.

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Marsh, Ian, Fengyi Li, and Gunnar Karlsson. "Wide Area Measurements of Voice over IP Quality." In Quality for All, 93–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45188-4_10.

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Conference papers on the topic "Voice quality"

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Gillett, Ben, and Simon King. "Transforming voice quality." In 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-21.

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Aikawa, Kiyoaki, Junko Uenuma, and Tomoko Akitake. "Acoustic correlates of voice quality improvement by voice training." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-750.

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Ueng, S. K., C. M. Luo, T. Y. Tsai, and H. Chang. "Voice Quality Assessment and Visualization." In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2012. http://dx.doi.org/10.1109/cisis.2012.62.

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Camargo, Zuleica, and Albert Rilliard. "Interactions of voice quality settings." In 10th International Conference of Experimental Linguistics. ExLing Society, 2019. http://dx.doi.org/10.36505/exling-2019/10/0010/000372.

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Campbell, Nick. "Accounting for voice-quality variation." In Speech Prosody 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/speechprosody.2004-50.

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Adas, Abdelnaser, and Amarnath Mukherjee. "Providing guaranteed quality of service for variable-bit-rate video at a multiplexor." In Voice, Video, and Data Communications, edited by Wai Sum Lai and Hisashi Kobayashi. SPIE, 1997. http://dx.doi.org/10.1117/12.290406.

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Koo, Chang-Hoi, Jae-Ho Lee, Kwang-Chae Park, and Kwon-Chul Park. "Evaluation of output strategy with separated buffer in ATM switching system (quality service lines)." In Voice, Video, and Data Communications, edited by Wai Sum Lai and Hisashi Kobayashi. SPIE, 1997. http://dx.doi.org/10.1117/12.290441.

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Lapidus, M., and I. Shallom. "Enhanced intrusive Voice Quality Estimation (EVQE)." In Electronics Engineers in Israel (IEEEI 2010). IEEE, 2010. http://dx.doi.org/10.1109/eeei.2010.5662174.

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Ramo, Anssi. "Voice quality evaluation of various codecs." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495201.

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Kostek, B., and P. Zwan. "Automatic classification of singing voice quality." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.28.

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Reports on the topic "Voice quality"

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Kang, G. S., and L. J. Fransen. High-Quality 800-b/s Voice Processing Algorithm. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada232352.

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Pendleton, A., A. Clark, A. Johnston, and H. Sinnreich. Session Initiation Protocol Event Package for Voice Quality Reporting. RFC Editor, November 2010. http://dx.doi.org/10.17487/rfc6035.

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Pieper, Jaden, Jesse Frey, Chelsea Greene, Zainab Soetan, Tim Thompson, Donald Bradshaw, and Stephen Voran. Mission critical voice quality of experience access time measurement methods. Gaithersburg, MD: National Institute of Standards and Technology, October 2019. http://dx.doi.org/10.6028/nist.ir.8275.

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Vaudreuil, G., and G. Parsons. Toll Quality Voice - 32 kbit/s ADPCM MIME Sub-type Registration. RFC Editor, September 1998. http://dx.doi.org/10.17487/rfc2422.

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Greene, Chelsea, Jesse Frey, Zainab Soetan, Jaden Pieper, and Tim Thompson. Mission Critical Voice Quality of Experience Access Time Measurement Method Addendum. National Institute of Standards and Technology, December 2020. http://dx.doi.org/10.6028/nist.ir.8328.

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Howarth, Gary. Mission Critical Voice Quality of Experience Probability of Successful Delivery Measurement Methods. Gaithersburg, MD: National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.tn.2259.

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Greene, Chelsea, Jesse Frey, William Magrogan, Cara O’Malley, and Jaden Pieper. Optimal Transmit Volume Conditions for Mission Critical Voice Quality of Experience Measurement Systems. National Institute of Standards and Technology, September 2021. http://dx.doi.org/10.6028/nist.tn.2171.

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Mann, Eric, and Jeffrey Paffrath. Vocal Cord Function and Voice Quality Evaluation of Active Duty U.S. Army Drill Instructors. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada303155.

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Vaudreuil, G., and G. Parsons. Toll Quality Voice - 32 kbit/s Adaptive Differential Pulse Code Modulation (ADPCM) MIME Sub-type Registration. RFC Editor, June 2004. http://dx.doi.org/10.17487/rfc3802.

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Fee, Kyle D. Does Job Quality Affect Occupational Mobility? Federal Reserve Bank of Cleveland, August 2022. http://dx.doi.org/10.26509/frbc-cd-20220804.

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Abstract:

Job quality, a well-known topic in workforce development circles, is an underutilized but useful lens with which to examine labor market conditions. The Aspen Institute (2020), a long-time advocate for job quality, defines it as “a range of attributes that drive worker experiences: wages, benefits, scheduling, legal rights, equity and inclusion, opportunity to build skills and advance, supportive work environment, and worker voice.” Given the record number of resignations and available job openings, especially in the lower-paid industry sectors, along with popular labor market narratives around the Great R’s (Resignation, Renegotiation, Reshuffle), I wonder to what extent job quality plays a role in the occupational mobility of workers. Occupational mobility includes all potential outcomes an individual has when holding a job. For instance, in addition to the option of changing to another occupation, an individual can remain in that occupation, become unemployed, or leave the labor force. Occupational mobility metrics are an appealing way to explore labor market conditions because they provide a dynamic perspective, while traditional metrics such as unemployment and labor force participation rates tend to be static observations.

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