Computational Social Linguistics for Telugu Cultural Preservation: Novel Algorithms for Chandassu Metrical Pattern Recognition

Boddu Sri Pavan; Boddu Swathi Sree

cs.CLSep 23, 2025

Computational Social Linguistics for Telugu Cultural Preservation: Novel Algorithms for Chandassu Metrical Pattern Recognition

Authors: Boddu Sri Pavan, Boddu Swathi Sree

Organizations: Independent Researcher · Department of Computer Science and Engineering RGUKT, Nuzvid

Abstract

This research presents a computational social science approach to preserving Telugu Chandassu, the metrical poetry tradition representing centuries of collective cultural intelligence. We develop the first comprehensive digital framework for analyzing Telugu prosodic patterns, bridging traditional community knowledge with modern computational methods. Our social computing approach involves collaborative dataset creation of 4,651 annotated padyams, expert-validated linguistic patterns, and culturally-informed algorithmic design. The framework includes AksharamTokenizer for prosody-aware tokenization, LaghuvuGuruvu Generator for classifying light and heavy syllables, and PadyaBhedam Checker for automated pattern recognition. Our algorithm achieves 91.73% accuracy on the proposed Chandassu Score, with evaluation metrics reflecting traditional literary standards. This work demonstrates how computational social science can preserve endangered cultural knowledge systems while enabling new forms of collective intelligence around literary heritage. The methodology offers insights for community-centered approaches to cultural preservation, supporting broader initiatives in digital humanities and socially-aware computing systems.

Figures & tables

Aksharam Token Count	Ganam Name	LaghuvuGuruvu Pattern
1	LA	\|
1	GA	U
2	LAA	\|\|
2	VA	\|U
2	HA	U\|
2	GAA	UU

Table 1: Ganam classification by aksharam token count and laghuvu-guruvu pattern

Prosodic Class	Padyam Types
Vruttamu	Vutpalamaala, Champakamaala, Saardulamu, Mattebhamu
Jaathi	Kandamu
Vupajaathi	Teytageethi, Aataveladi, Seesamu

Table 2: Telugu prosodic classes and types in Chandassu dataset

Figure 1: Distribution of padyams across satakams in Chandassu dataset

Prosodic Class	Padyam Count
Vruttamu	1625
Jaathi	683
Vupajaathi	2343

Table 3: Distribution of padyams by prosodic class in Chandassu dataset

Padyam Type	Padyam Count
Vutpalamaala	329
Champakamaala	389
Saardulamu	290
Mattebhamu	617
Kandamu	683
Teytageethi	676

Table 4: Distribution of padyams by type in Chandassu dataset

Figure 2: Telugu character set used by the proposed framework

Figure 3: Proposed computational framework architecture for Telugu metrical poetry analysis

Figure 4: Performance evaluation across prosodic classes

Figure 5: Performance evaluation across padyam types

Prosodic Class	$c_{na}$	$c_{np}$	$c_{gk}$	$c_{yt}$	$c_{pr}$	$C$
Vruttamu	99.43	99.94	93.78	81.38	96.12	94.13
Jaathi	-	100.00	92.85	73.94	90.78	89.39
Vupajaathi	-	99.91	94.12	78.21	-	90.75

Table 5: Performance evaluation by prosodic class (values in %)

Padyam Type	$c_{na}$	$c_{np}$	$c_{gk}$	$c_{yt}$	$c_{pr}$	$C$
Vutpalamaala	98.97	99.70	91.87	77.36	94.60	92.50
Champakamaala	99.54	100.00	96.02	83.48	95.69	94.95
Saardulamu	99.35	100.00	94.88	79.66	97.59	94.29
Mattebhamu	99.63	100.00	92.89	83.02	96.52	94.41
Kandamu	-	100.00	92.85	73.94	90.78	89.39
Teytageethi	-	99.85	92.85	76.81	-	89.84

Table 6: Performance evaluation by padyam type (values in %)

Cultural Heritage

Cultural heritage uses AI to document, analyze, preserve, and interpret artifacts, sites, archives, and living traditions.

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ShlokaDhara: A Multi-Speaker Sanskrit Speech Corpus for Learning Meter-Aware Prosody in Text-to-Recitation

Harshal Kausadikar, Onkar Kishor Susladkar, Prathmesh Santosh Choudhari, Vedangi Deepak Deshpande+2

Independent Researcher · University of Illinois at Urbana-Champaign · University of Florida+2

Speech Processing TTS Synthesis Speech Corpus Construction TTS Evaluation

Open paper AskPDF arXiv

Sanskrit verse recitation is governed by

\textit{Chandas}

, a strict prosodic framework defined by rhythmic

\textit{Laghu}

(light) and

\textit{Guru}

(heavy) syllables. Because existing Sanskrit speech datasets prioritize ASR or conversational TTS, they lack the metrical fidelity required for authentic verse synthesis. We introduce

\textbf{

ShlokaDhara

}

, a 43-hour, multi-speaker Sanskrit speech corpus capturing diverse meters, recitation styles, and melodic variations. We specifically focus on

\textit{Text-to-Recite}

, a synthesis task explicitly requiring both prosodic accuracy and metrical correctness. Benchmarking state-of-the-art TTS architectures via an automated UTMOS (neural MOS) predictor demonstrates that XTTS-v2 achieves the highest perceptual quality. We further validate the XTTS-v2 generated samples using two paradigms: subjective assessments by 16 Sanskrit experts and novel empirical metrics, specifically, syllable-level

\textit{Laghu-Guru}

duration analysis to objectively quantify structural metrical awareness. Ultimately, this work establishes a robust dataset and evaluation benchmark for prosody-aware computational Sanskrit recitation. Generated Samples: https://shlokadhara.vercel.app/

Abstract

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