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NATS 2022 Posters Supplementary Materials

Welcome to the QR code supplementary materials link from my research posters! Scroll down below & view each section to find more information about each of the 3 research posters I am presenting at the NATS 2022 Conference this weekend. Thank you for your interest in my work; I'm grateful to you for stopping by. Please, stay in touch! Feel free to email me at theodoranestorova@gmail.com.

Day 3 | Monday, July 4th

Convergences in the Articulatory Settings of Bulgarian, Russian, & English:

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Bulgarian Lyric Diction as an Accessible Gateway to Singing in Cyrillic

BULGARIAN Reference Audio GuideSilent Night Spoken
00:00 / 00:29
RUSSIAN Reference Audio GuideSilent Night Spoken
00:00 / 00:21

Try it yourself! Which did you find more approachable to decipher, speak, & sing?

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To evaluate the convergences and divergences of articulatory settings and acoustic properties of sung and spoken Bulgarian, Russian, and North American English.

 

Deciphering Cyrillic script and Slavic phonemes are often barriers to accessibility of non-Western repertoire for classical singers. Of all Slavic languages, Bulgarian phonology has the least number of distinctive sounds. Despite this, Bulgarian, like other lesser-known Slavic languages (such as Ukrainian, Serbian, Croatian, Montenegrin, and more) remain underrepresented, tending

to be overshadowed by more well-known Slavic languages and repertoires already steeped in the classical music canon. While these may be unfamiliar languages and underexplored repertoires in the North American voice training studio, they stand to be elevated.

 

A pilot study involving four native English-speaking graduate classical singers with prior experience interpreting International Phonetic Alphabet (IPA) were divided into two equally weighted groups. Participants did not have prior experience with Russian or Bulgarian languages. Singers were provided annotated scores (IPA transcription, transliteration, and English translation) and professional example recordings of an excerpt from “Silent Night” in English, Russian, and Bulgarian languages.

 

The first participant group (G1) listened to the recordings and practiced the text once in both speaking and singing. Then, they recited and sang the excerpt in 1.) Bulgarian and 2.) Russian. The second participant group (G2) completed the protocol in the opposite order (Russian first, then Bulgarian).

 

Once completing the protocol, singers were asked to fill out a self-reported learning experience survey. A Russian and a Bulgarian lyric diction specialist rated each participant’s linguistic accuracy using a 1 (correct), 0 (incorrect) scale per phoneme.

Preliminary statistical results indicate a strong correlation between the raters’ linguistic accuracy scores and singers’ self-reporting scores in both groups. LTAS and MRI analysis revealed more similarities in articulatory setting configuration between English and Bulgarian than English and Russian. All singers in both groups had a higher Bulgarian linguistic accuracy score than Russian. G1 singers scored a higher Russian linguistic accuracy and self-reporting score.

 

The results of the present pilot study suggest Bulgarian’s converging phonetic construction with English. Studying Bulgarian lyric diction first may serve as an effective pathway for singers in training who wish to approach other Slavic languages written in Cyrillic script. The Bulgarian lyric diction and repertoire warrant further investigation regarding its pedagogical benefits.

Take a watch/listen to some Bulgarian art songs that are very appropriate for singers in training!

For more information on Slavic repertoire & lyric diction, contact me, or explore the first volume (more to come soon!) of the Bulgarian Art Song Anthology.

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BONUS: below, you will find audio & video recordings of traditional Bulgarian folk singing, performed by my great-grandmother, Atanaska Todorova, a self-taught singers & one of the first musicians to record at the Bulgarian National Radio in Sofia in the early 1900s.

Day 2 |

Sunday, July 3rd

Vibrato Variability as a Diagnostic Teaching Tool: Perception of Production & Expression

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Let's play: Guess That Genre!

Opera? Mus. Theat.? Jazz?
Opera? Mus. Theat.? Jazz?
Opera? Mus. Theat.? Jazz?

Let's play: What Do You Hear in the Production & Expression?

What do you hear?
What do you hear?
What do you hear?

Did you mention vibrato? How so?

Check out the latest peer-reviewed article/editorial on vibrato authored by Theodora Nestorova in the ACTOR Project Timbre Lingo Series:

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Figs. 3 & 4 | Middle Panel Biphasic SampleJazz
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Uniform (Monophasic)Bulgarian Folk
00:00 / 00:03
Increasing (Biphasic)Musical Theatre
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Highly Complex (Qudriphasic)Jazz
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You tell me, anonymously!:

  1. What interests you about vibrato?

  2. What aspect of vibrato would you like to know more about?

Day 1 | Saturday, July 2nd

Analysis & Interpretation of Complex Vibrato Patterns:
A Novel Parametric Approach to Genre-Specific Variability

A complex, under-researched phenomenon, vibrato exists in a variety of musical styles and genre contexts. However, voice researchers have historically analyzed vibrato with tools presuming a western classical opera aesthetic. Current analytical methods using average metrics are applicable only if the vibrato is uniform, consistent, and persistent. This disregards significant stylistic characteristics of many performed genres with non-normative vibrato features. Therefore, a new system of vibrato metrics considering the regularity, variability, and shape of vibrato in more genres over time is essential.

A performance task list of two cross-genre songs and one vocal exercise was disseminated to fifteen professional operatic, musical theater, and jazz singers. Sixteen pitch segments from each singer were subjected to sinusoidal extraction, fo band-pass filtering, and an FFT LTAS in Praat. Mean half extent (in cents), pitch, vowel, style/singer subject was calculated for each sample and assessed using standard deviation, Coefficient of Variation (CV%), linear and polynomial regression, and non-linear regression techniques in R.

The results indicated that vibrato variability predictably distinguishes performed genres. The CV% well characterizes vibrato variability and is higher in the samples of Musical Theater and Jazz singers. A novel model – 4 parameter logistic s-curve regression – is proposed as a representation of such multi-phasic vibrato with complex shapes.

A perceptual survey using samples most representative of each genre’s average CV% was distributed to seven vocal pedagogue judges, confirming that Jazz and Musical Theater singers’ vibrato variability is more perceivable and accurately categorized when compared to Opera singers’ vibrato.

Such novel, perceptually-correlated vibrato models may be employed to examine and evaluate complex vibrato patterns, style-specific performance, in turn promoting more genre-inclusive voice training.

All from bottom right panel: