The Relationship between Consciousness and Linguistic Data to Formalize

I. INTRODUCTION

1.1 Scientific Background

Uncovering the relationship between language and consciousness is a complex challenge in modern linguistics and cognitive science. To understand this relationship, phonology, morphophonology, and formal linguistics offer a solid theoretical foundation. Phonology—especially metrical phonology and prosody—examines how sounds and rhythmic patterns are organized in language, while morphophonology studies phonological alternations in word forms. These domains reveal that symmetrical patterns are often observable in linguistic structures: for instance, alliteration lends the text a "translational" symmetry [1], and rhyme schemes may feature mirror-like repetitions (e.g., the "abba" enclosed rhyme). Similarly, among the basic units of versification—the metrical feet—we find both symmetric and asymmetric structures: the former exemplified by the pyrrhic or the spondee, the latter by the iamb and the trochee. [1] Formal linguistics seeks to describe these phenomena precisely through formal models (e.g., generative grammar, algebraic descriptions of language) that enable a rigorous, mathematically grounded representation of linguistic patterns. This scientific background suggests that there are systematic, formalizable correspondences between linguistic structures and the mental processes underlying them—including consciousness.

1.2 Applied Methodology

To achieve the stated aim, the study draws on the findings and methods of three main disciplines, which together provide a symmetry-based framework for the investigation:

• Computational encoding and symmetry-based linguistic classification: The first pillar is a computational linguistic analysis that encodes and classifies linguistic elements on the basis of syllable typology and position within the word. In practice, words and structures are transformed into formal codes that reflect the symmetric patterns inherent in them (for example, the symmetry of their syllable structure or recurring elements). This approach enables us to identify systematic groupings in the linguistic data and to explore the extent to which certain patterns (e.g., palindromic or rhythmic symmetries) are deliberately crafted or arise spontaneously in language use.

• Rhythm-based phonological analysis: The second pillar examines the rhythmic organization of language at the intersection of phonology and metrics (versification). Specifically, we analyze the Omagyar Mária-siralom as a case study, a text of poetic-historical significance whose verse rhythm, according to research, exhibits a distinctly iambic tendency. [2] Our analysis pays particular attention to the alternation and role of iambic and trochaic rhythms in this work. Comparing iambic (weak-strong stress pattern) and trochaic (strong-weak) cadences can reveal how symmetric and asymmetric rhythmic structures manifest in language, and how they may contribute to meaning-making or to consciously perceived effects in the listener. Metrical and phonological analysis not only maps the rhythm of the historical text but also permits broader conclusions about the extent to which linguistic rhythm may be the product of conscious linguistic composition.

• Linguistic melody and historical comparison: The third pillar investigates linguistic melody—that is, pitch and intonation in linguistic expression—with special attention to

the relationship between verse and melody. Within this framework, through historical examples, we examine how metrical form (meter, rhythm) relates to the melody of speech (prosodic patterns). For instance, prior musicological and literary scholarship has attempted to reconstruct the presumed melody of the Omagyar Maria-siralom, shedding light on the melodic aspects of how texts of the period were performed. In studying linguistic melody, we compare the roles of intonation and pitch across the versification of different eras and languages-by analyzing, for example, medieval hymns, folk hymns, and selected works of classical Hungarian poetry. This historical-comparative approach aims to determine the extent to which melodicity is a structural (and perhaps symmetric-describable) feature of language, and how it contributes to the recipient's conscious experience (the conveyance of emotions and affective tone).

These three methodological pillars form a close unity: computational encoding provides a quantitative basis for identifying linguistic patterns, while rhythm analysis and melody study supply a qualitative, interpretive framework that links these patterns to the concept of consciousness. Symmetry theory serves as a common denominator: whether text, sound, or structure is at issue, patterns of symmetry and repetition/variation are interpretable across all three areas. In this way, our methodology integrates formal and empirical approaches: computational modelling captures objective symmetries in language, while phonological-metrical analysis illuminates their conscious and aesthetic dimensions.

1.3 Practical Applicability

The research is not only of theoretical significance; its results can also be leveraged across numerous practical domains. Below, we highlight the most important avenues of application:

• Speech recognition and speech synthesis: In speech technology, it is crucial to account for the rhythmic and melodic (prosodic) characteristics of language. Adequate modelling of prosody is indispensable for producing natural-sounding synthetic speech [3], yet today's speech-recognition algorithms exploit the information contained in rhythm and pitch only to a limited extent [3]. Our research can contribute to more advanced recognition systems that identify speech more accurately by detecting symmetry-based rhythmic patterns, as well as to synthesis solutions that incorporate a text's melody and rhythm to produce a more natural, human-like sound.

• Natural language processing (NLP): Formal linguistic models—especially when they integrate aspects of conscious processes—can improve the performance of computational language-processing systems. Symmetry-based linguistic analysis can aid in a better understanding of sentence structures, word-form alternations, and stress patterns, which is useful for tuning translation algorithms, automatic summarizers, and grammatical parsers. This can bring us closer to machine models that also take into account the consciousness-driven aspects of human language use (for example, what pragmatic purpose a given emphasis or word-order change serves).

• Rhythmic text synthesis: The automatic generation of poetic language or rhythmic prose is a distinct domain in which the present research can be applied directly. Formalized knowledge of versification rules and rhythmic patterns makes it possible to develop algorithms capable of producing text in accentual (stress-based) or even quantitative styles. This has practical value for creative-writing applications, poetic-style chatbots, and even for emulating the rhythmic style of literary works. Rhythm-driven text generation can provide not only an aesthetic experience but also support language learning by making the roles of stress and rhythm in language more tangible for learners.

• Literary-historical interpretations: The research can also offer valuable insights into literary studies and philology. By reconstructing the rhythm and melody of historical texts (e.g., medieval poems and hymns), the circumstances of composition and mechanisms of effect for certain works can be reinterpreted. If we formally describe an old text's rhythmic-melodic patterning, we can better understand which conscious compositional principles may have guided the author, and what effect the work may have had on its contemporary audience. For example, the results of the rhythmic and melodic analysis of the Omagyar Mária-siralom can help us examine this work not only as a relic of language history but also as a poem intended for oral performance, bringing its message closer to today's audiences. The knowledge thus gained enriches literary-historical interpretations and can open a new dialogue between linguistic formalism and literary analysis.

In summary, the study's interdisciplinary approach—which combines computational linguistic encoding, rhythm-based phonology, and the study of linguistic melody—enables a more comprehensive understanding and formalization of the relationship between linguistic data and consciousness. The symmetry-based framework helps to place phenomena observed at different linguistic levels on a unified theoretical footing, uncovering deeper interrelations between structure and meaning, form and function. The introduction presented here is a prelude to the analyses to be detailed later, grounding the hypothesis that the patterns of the conscious shaping of language can indeed be captured and systematized within a symmetry-theoretic model—one that both enriches our theoretical knowledge and leads to practical applications.

An integrated view of the disciplines of machine code, rhythm-based phonology, and linguistic melody is indispensable for terminals to communicate accurately and quickly:

II. MACHINE ENCODING

2.1 The Model

Consciousness (search) $=[1:2]:[3:4]$

$1=$ Syllable template
2= Word-initial/medial/final syllables
$3=$ Word-relative syllable positions
$4=$ Word classes by syllable count

2.2 The Description

We classify words into word types based on their number of syllables:

D word, A word, B1 word, B2 word, B3 word, B4 word, B5 word, B6 word, B7 word, B8 word (e.g. D word = 'sztrajk'; A word = 'labda'; B1 word = 'ballada'; B2 word = 'levendula' etc.)

From these word types, we derive a classification of syllables into 55 syllable types:

D syllable;

A1 syllable, A2 syllable, A3 syllable, A4 syllable, A5 syllable, A6 syllable,

A7 syllable, A8 syllable, A9 syllable;

B1 syllable, B(2-1) syllable, B(2-2) syllable, B(3-1) syllable, B(3-2) syllable,

B(3-3) syllable, B(4-1) syllable, B(4-2) syllable, B(4-3) syllable, B(4-4) syllable,

B(5-1) syllable, B(5-2) syllable, B(5-3) syllable, B(5-4) syllable, B(5-5) syllable,

B(6-1) syllable, B(6-2) syllable, B(6-3) syllable, B(6-4) syllable, B(6-5) syllable,

B(6-6) syllable, B(7-1) syllable, B(7-2) syllable, B(7-3) syllable, B(7-4) syllable,

B(7-5) syllable, B(7-6) syllable, B(7-7) syllable, B(8-1) syllable, B(8-2) syllable,

B(8-3) syllable, B(8-4) syllable, B(8-5) syllable, B(8-6) syllable, B(8-7) syllable,

B(8-8) syllable;

C1 syllable, C2 syllable, C3 syllable, C4 syllable, C5 syllable, C6 syllable,

C7 syllable, C8 syllable, C9 syllable.

(E.g. D syllable = 'sztrájk';

A1 syllable = 'lab-'; A2 syllable = 'bal-'; A3 syllable = 'le-' etc.

B1 syllable = 'la-'; B(2-1) syllable = 'ven-'; B(2-2) syllable = 'du-' etc.

C1 syllable = 'da-'; C2 syllable = 'da-'; C3 syllable = 'la-' etc.)

We further classify syllables into five syllable classes (V = vowel, C = consonant):

1. reduced syllable (V);
2. "inorganic" (consonant-only) syllable (C; CC; CCC; CCCC);
3. open syllable (CV; CCV; CCCV);
4. closed syllable (VC; VCC; VCCC);
5. full syllable (CVC; CVCC; CVCCC; CCVC; CCVCC; CCVCCC; CCCVC; CCCVCC)

Cf. examples: $\mathrm{V}={}^{\prime }\mathrm{a}-{}^{\prime }$ ; $\mathrm{C}={}^{\prime }\mathrm{s}-{}^{\prime }$ ; $\mathrm{CC}={}^{\prime }\mathrm{hm}-{}^{\prime }$ ; $\mathrm{CCC}={}^{\prime }\mathrm{pszt}-{}^{\prime }$ ; $\mathrm{CCCC}={}^{\prime }\mathrm{sscc}-{}^{\prime }$

$\mathrm{CV}=\mathrm{ka}-;\mathrm{CCV}=\mathrm{sta}-;\mathrm{CCCV}=\mathrm{stra}-;\mathrm{VC}=\mathrm{asz}-;\mathrm{VCC}=\mathrm{ing}-;\mathrm{VCCC}=\mathrm{inst}-;.$

CVC = 'tal-'; CVCC = 'rend-'; CVCCC = 'monst-'; CCVC = 'kris-';

CCVCC = 'sport-'; CCVCCC = 'szkunksz-'; CCCVC = 'skrib-'; CCCVCC = 'strand-'.

Compare the notation of syllable fonts with examples:

$\mathrm{V}={}^{\prime }\mathrm{a}-{}^{\prime };$

$\mathrm{C}={}^{\prime }\mathrm{s}{-}^{\prime };$

CC = 'hm-';

[ $\mathrm{CCC}$ = {}^{\prime}$\mathrm{pszt}$ - {}^{\prime}; < $\mathrm{pszt}$; < $\mathrm{p}$ < $\mathrm{sz}$; $\mathrm{t}$ > ]

[ $\mathrm{CV}$ = {}^{\prime}$\mathrm{ka}$ - {}^{\prime}; < $\mathrm{ka}$ >; < $\mathrm{k}$ < $\mathrm{a}$ > ]

CCV = 'sta-'; ; <s < t

$\mathrm{VC}={}^{\prime }$ asz- ${\cdot }^{\text{一}}$ .<asz .sz>

CVC = 'tal-'; ; <t 1 >

CVCC = 'rend-'; ; <r n > d >

CVCCC = 'monst-'; ; <m n > s > t >

CCVC = 'kris-'; ;<k<rs>

CCVCC = 'sport-'; ; <s <p r > t >

CCVCCC = ' szkunksz-'; ; <sz < k n > k > sz >

CCCVC = 'skrib-'; ; <s <k <r b >

CCCVCC = 'strand-'; ; <s <t <r n > d >

This notation reflects the center-periphery model, in which vowels (V) are central and consonants (C) surround them in symmetrical layers. It supports linguistic modelling, phonological analysis, and database structuring for applications such as speech-to-text systems.

The database's code system is constructed by means of a symmetry-based classification method

a) The position within the syllable of consonants located on the periphery:

1. CVC-1 - the first consonant of the full syllable (occurs before the vowel)

2. CVC-2 - the second consonant of the full syllable (occurs after the vowel)

3. $CVCC-1$ – the first consonant of the full syllable (occurs before the vowel)

4. $CVCC-2$ – the second consonant of the full syllable (occurs after the vowel)

5. $CVCC-3$ – the third consonant of the full syllable (occurs after the vowel)

6. etc.
   b) Encoded notation of syllable classes for vowels, e.g.:

CVCC:D, CVC:D, CV:D, V:D, VC:D, CVC:A1, CV:A1, V:A1, VC:A1, CVCC:C1, CVC:C1, CV:C1, V:C1, VC:C1, CVC:A2, CV:A2, V:A2, VC:A2, CVCC:B(1), CVC:B(1), CV:B(1), V:B(1), VC:B(1), CVC:C2, CV:C2, VC:C2, CV:A3, V:A3, CVC:B(2-1), CV:B(2-1), V:B(2-1), CVC:B(2-2), CV:B(2-2), VC:B(2-2), CVC:C3, CV:C3, etc.

This format—syllable structure: syllable type—offers a compact and highly systematic way to classify syllables both by internal composition and by their position within words.

Examples:

$\gg$ CVCC: $D$ a full syllable in a one-syllable word form
» CVC:A1 – the word-initial full syllable in a two-syllable word form
» CVC:A2 – the word-initial full syllable in a three-syllable word form
$\gg$ CVC: $C1$ - the word-final full syllable in a two-syllable word form
$\gg$ CVC: $C3$ - the word-final full syllable in a four-syllable word form
$\gg$ CV: C1$-theword-finalopensyllableinatwo-syllablewordform$\gg$VC:A2-theword-initialclosedsyllableinathree-syllablewordform$\gg V: D$-areducedsyllableinaone-syllablewordform$\gg$CVCC:$B(1)$-aword-internalfullsyllableinathree-syllablewordform$\gg$CVC:$B(2-1)$-thefirstword-internalfullsyllableinafour-syllablewordform$\gg$CVC:$B(2-2)$ - the second word-internal full syllable in a four-syllable word form, etc.

This system enables precise and scalable classification of syllables based on both phonological structure and positional function within words.

c) Encoded notation of syllable classes for consonants, e.g.:

CVCC-1:D, CVCC-2:D, CVCC-3:D, CVC-1:D, CVC-2:D, CV-1:D, VC-1:A1, CVC-2:A1, CV-1:A1, VC-1:A1, CVCC-1:C1, CVCC-2:C1, CVCC-3:C1, CVC-1:C1, CVC-2:C1, CV-1:C1,

VC-1:C1, CVC-1:A2, CVC-2:A2, CV-1:A2, VC-1:A2, CVCC-1:B(1), CVCC-2:B(1), CVCC-3:B(1), CVC-1:B(1), CVC-2:B(1), CV-1:B(1), CVC-1:C2, CVC-2:C2, CV-1:C2, VC-1:C2,

CV-1:A3,CVC-1:B(2-1),CVC-2:B(2-1),CV-1:B(2-1),CVC-1:B(2-2),CVC-2:B(2-2),

CV-1:B(2-2),VC-1:B(2-2),CVC-1:C3,CVC-2:C3, CV-1:C3,etc.

This notation makes it possible to determine the exact position of a consonant within a syllable and its role in the larger word structure—capabilities that are indispensable for accurate phonological encoding, analysis, and synthesis in computational linguistics and speech technologies.

III. RHYTHM-BASED PHONOLOGY

Following Róbert Gragger, it has been a widely held view in the scholarship for 100 years that—on content grounds—we do not know the Latin model of the poem titled Ómagyar Mária-siralom (hereafter OMS), while its rhythm is also surprising: “Für die Rhythmik ist unsere Marienklage eine Überraschung.” [4] From this, it follows that there is no demonstrable evidence that the author of OMS knew its source.

The difficulty arises from the conflation of an accentual and a quantitative incipit: "Es ist beachtenswert, wie tadellos das in trochäische Form gezwungene Gedicht mit ungarischer Takteinteilung klingt." [5] Thus, both the initial-stress accent and the long quantity fall on the first syllable of the metrical foot. PAN (Planctus ante nescia) employs a simultaneous system of versification, whereas OMS does not. I compared the versification of the complete Planctus ante nescia (hereafter PAN), prepared on the basis of the critical edition of the Carmina Burana, with the Hungarian versification of OMS. [6] The result was that, in place of Latin trochees, I found iambs in Hungarian, while elsewhere dactyls were rendered by dactyls. In the Hungarian verse form, only the quantitative system of versification emerged.

Highlighting the phonological examples below, I analyze typically doubtful word forms:

buntelen-byuntelen

+ <$\ddot{u}$>n +t^{c}n.