Emotional expressivity in singing. Assessing physiological and acoustic indicators of two opera singers' voice characteristics.

In an earlier study, we analyzed how audio signals obtained from three professional opera singers varied when they sang one octave wide eight-tone scales in ten different emotional colors. The results showed systematic variations in voice source and long-term-average spectrum (LTAS) parameters associated with major emotion "families". For two of the singers, subglottal pressure (PSub) also was recorded, thus allowing analysis of an additional main physiological voice control parameter, glottal resistance (defined as the ratio between PSub and glottal flow), and related to glottal adduction. In the present study, we analyze voice source and LTAS parameters derived from the audio signal and their correlation with Psub and glottal resistance. The measured parameters showed a systematic relationship with the four emotion families observed in our previous study. They also varied systematically with values of the ten emotions along the valence, power, and arousal dimensions; valence showed a significant correlation with the ratio between acoustic voice source energy and subglottal pressure, while Power varied significantly with sound level and two measures related to the spectral dominance of the lowest spectrum partial. the fundamental.

Analyzing Emotion Expression in Singing via Flow Glottograms, Long-Term-Average Spectra, and Expert Listener Evaluation.

BACKGROUND: Acoustic aspects of emotional expressivity in speech have been analyzed extensively during recent decades. Emotional coloring is an important if not the most important property of sung performance, and therefore strictly controlled. Hence, emotional expressivity in singing may promote a deeper insight into vocal signaling of emotions. Furthermore, physiological voice source parameters can be assumed to facilitate the understanding of acoustical characteristics. METHOD: Three highly experienced professional male singers sang scales on the vowel /ae/ or /a/ in 10 emotional colors (Neutral, Sadness, Tender, Calm, Joy, Contempt, Fear, Pride, Love, Arousal, and Anger). Sixteen voice experts classified the scales in a forced-choice listening test, and the result was compared with long-term-average spectrum (LTAS) parameters and with voice source parameters, derived from flow glottograms (FLOGG) that were obtained from inverse filtering the audio signal. RESULTS: On the basis of component analysis, the emotions could be grouped into four "families", Anger-Contempt, Joy-Love-Pride, Calm-Tender-Neutral and Sad-Fear. Recognition of the intended emotion families by listeners reached accuracy levels far beyond chance level. For the LTAS and FLOGG parameters, vocal loudness had a paramount influence on all. Also after partialing out this factor, some significant correlations were found between FLOGG and LTAS parameters. These parameters could be sorted into groups that were associated with the emotion families. CONCLUSIONS: (i) Both LTAS and FLOGG parameters varied significantly with the enactment intentions of the singers. (ii) Some aspects of the voice source are reflected in LTAS parameters. (iii) LTAS parameters affect listener judgment of the enacted emotions and the accuracy of the intended emotional coloring.

Prediction of three articulatory categories in vocal sound imitations using models for auditory receptive fields.

Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, the classification of three articulatory categories, phonation, supraglottal myoelastic vibrations, and turbulence, have been modeled from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments. The audio data were manually annotated by two experienced phoneticians using a detailed articulatory description scheme. A separate set of audio features was developed specifically for each category using both time-domain and spectral methods. For all time-frequency transformations, and for some secondary processing, the recently developed Auditory Receptive Fields Toolbox was used. Three different machine learning methods were applied for predicting the final articulatory categories. The result with the best generalization was found using an ensemble of multilayer perceptrons. The cross-validated classification accuracy was 96.8% for phonation, 90.8% for supraglottal myoelastic vibrations, and 89.0% for turbulence using all the 84 developed features. A final feature reduction to 22 features yielded similar results.

Natural Voice Use in Patients With Voice Disorders and Vocally Healthy Speakers Based on 2 Days Voice Accumulator Information From a Database.

OBJECTIVES AND STUDY DESIGN: Information about how patients with voice disorders use their voices in natural communicative situations is scarce. Such long-term data have for the first time been uploaded to a central database from different hospitals in Sweden. The purpose was to investigate the potential use of a large set of long-term data for establishing reference values regarding voice use in natural situations. METHODS: VoxLog (Sonvox AB, Umeå, Sweden) was tested for deployment in clinical practice by speech-language pathologists working at nine hospitals in Sweden. Files from 20 patients (16 females and 4 males) with functional, organic, or neurological voice disorders and 10 vocally healthy individuals (eight females and two males) were uploaded to a remote central database. All participants had vocally demanding occupations and had been monitored for more than 2 days. The total recording time was 681 hours and 50 minutes. Data on fundamental frequency (F0, Hz), phonation time (seconds and percentage), voice sound pressure level (SPL, dB), and background noise level (dB) were analyzed for each recorded day and compared between the 2 days. Variations across each day were measured using coefficients of variation. RESULTS: Average F0, voice SPL, and especially the level of background noise varied considerably for all participants across each day. Average F0 and voice SPL were considerably higher than reference values from laboratory recordings. CONCLUSIONS: The use of a remote central database and strict protocols can accelerate data collection from larger groups of participants and contribute to establishing reference values regarding voice use in natural situations and from patients with voice disorders. Information about activities and voice symptoms would supplement the objective data and is recommended in future studies.

What do male singers mean by modal and falsetto register? An investigation of the glottal voice source.

The voice source differs between modal and falsetto registers, but singers often try to reduce the associated timbral differences, some even doubting that there are any. A total of 54 vowel sounds sung in falsetto and modal register by 13 male more or less experienced choir singers were analyzed by inverse filtering and electroglottography. Closed quotient, maximum flow declination rate, peak-to-peak airflow amplitude, normalized amplitude quotient, and level difference between the two lowest source spectrum partials were determined, and systematic differences were found in all singers, regardless of experience of singing. The observations seem compatible with previous observations of thicker vocal folds in modal register.

Relation between perceived voice register and flow glottogram parameters in males.

The perception of modal and falsetto registers was analyzed in a material consisting of a total of 104 vowel sounds sung by 13 choir singers, 52 sung in modal register, and 52 in falsetto register. These vowel sounds were classified by 16 expert listeners in a forced choice test and the number of votes for modal was compared to the voice source parameters: (1) closed quotient (Q(closed)), (2) level difference between the two lowest source spectrum partials (H1-H2), (3) AC amplitude, (4) maximum flow declination rate (MFDR), and (5) normalized amplitude quotient (NAQ, AC amplitude/MFDR(*) fundamental frequency). Tones with a high value of Q(closed) and low values of H1-H2 and of NAQ were typically associated with high number of votes for modal register, and vice versa, Q(closed) showing the strongest correlation. Some singer subjects produced tones that could not be classified as either falsetto or modal register, suggesting that classification of registers is not always feasible.

Uma proposta de atuaçäo fonoaudiológica junto aos pacientes portadores da disfunçäo da articulaçäo têmporomandibular (ATM) considerando-se a relaçäo respiraçäo/variaçöes específicas do tônus muscular nestes quadros / Phonoaudiologic actuation in temporo-mandibular joint dysfunction patients

A partir da constataçäo da correlaçäo entre o processo respiratório e as variaçöes específicas do tônus muscular em indivíduos portadores de disfunçäo da ATM, a autora propöe formas de atuaçäo terapêutica que visam integrar os processos motores relacionados à Respiraçäo e à Articulaçäo Têmporo-Mandibular