BanglaIPA: Bengali to IPA Transcription

• BanglaIPA is a system that converts Bengali text into IPA symbols, resolving phonetic ambiguities and accommodating regional dialect variations.
• It employs neural sequence-to-sequence models with contextual numeral rewriting and STAT alignment to achieve precise grapheme-to-phoneme mapping.
• Empirical evaluations demonstrate significantly reduced Word Error Rates compared to alternatives, ensuring robust transcription in diverse language contexts.

BanglaIPA refers to methodologies and systems for the automated transcription of Bengali (Bangla) text into the International Phonetic Alphabet (IPA), targeting both standard and regional dialects, as well as numerical expressions. Such systems facilitate phonetic annotation, pronunciation modeling, automatic speech recognition (ASR), and text-to-speech (TTS), and serve as key resources for linguistic research, education, and downstream language technologies.

1. Linguistic and Technical Foundations

Bengali orthography presents a complex grapheme-to-phoneme mapping marked by phonological variation, context-dependent allophony, orthographic ambiguity, and significant dialectal diversity. The IPA provides a standardized way to represent these phonetic distinctions.

Core Phoneme Inventory

A rigorous inventory of Bengali phonemes (as per (Fatema et al., 2024)) includes:

• Vowels: 11 oral (e.g., ɐ, a, ɪ, i, u, uː, e, o, æ, ɔ, diphthongs oi, ou) and 7 nasalized counterparts.
• Semi-vowels/Glides: e.g., u̯, ɪ̯, e̯, o̯.
• Consonants: Full set of plosives (p, pʰ, b, bʱ, etc.), nasals (m, n̪, n, ɲ, ŋ), fricatives (s, ʃ, f, v, z, h), taps/flaps (ɾ), lateral (l), and approximants (j).
• Diphthongs: 31 total, including both regular and irregular (regional) forms.

Grapheme-to-Phoneme Function

The mapping is typically formalized as $f : G \rightarrow P$, where $G$ is the set of Bengali graphemes and $P$ the set of IPA symbols:

• $f(অ) = ɐ$
• $f(আ) = a$
• $f(ই) = ɪ$
• $f(ঈ) = iː$
• $f(উ) = u$
• $f(ঊ) = uː$
• ...
• $f(ফ) = pʰ$ (or $f$ for loans)
• $f(ভ) = bʱ$ (or $v$ in loan contexts)

The function must account for allophonic variation, dialectal phoneme shifts, and context-induced changes (Fatema et al., 2024). Strict diacritic ordering and syllabification are enforced for correct IPA.

2. BanglaIPA Systems: Architectural Trends

Modern BanglaIPA systems apply neural sequence-to-sequence models and modular pipelines to address the complexity of Bengali IPA transcription. The evolution traces from character-level approaches (Hasan et al., 2023), through dialect-aware architectures using region tokens (Islam et al., 2024), to recent modular pipelines emphasizing context and alignment (Hasan et al., 5 Jan 2026).

System Pipeline

A canonical BanglaIPA system (Hasan et al., 5 Jan 2026) consists of:

1. Contextual Numeral Rewriting: Bengali numerals are contextually rewritten as word forms using a compact LLM (e.g., GPT-4.1-nano).
2. Deduplication (Caching): Word-level caching leverages a precomputed dictionary mapping words to IPA strings, reducing redundant computations.
3. State Alignment (STAT Algorithm): The word is segmented into subwords; segments comprising only Bengali characters are flagged for G2P mapping, while others (digits, punctuation, foreign substrings) bypass the neural model.
4. IPA Generation: Segments requiring transcription are passed to a transformer-based sequence-to-sequence model.
5. Re-assembly: Output IPA segments and passthrough tokens are merged per the original sequence, preserving structure.

This modular flow achieves both robustness and computational efficiency (Hasan et al., 5 Jan 2026).

3. Model Architectures and Training

Sequence-to-sequence transformer architectures dominate BanglaIPA research. Distinct approaches include:

• Lightweight Transformer (Char-level): A single-layer encoder-decoder model (8.5M parameters) for independent word-wise IPA mapping (Hasan et al., 2023).
  • Input/output: max length 64, 136 Bangla chars $\rightarrow$ 56 IPA symbols.
  • Manual handling of punctuation and foreign tokens.
• Contextual and Subword-Aligned Models: STAT-based segmenters enable efficient hybrid handling of mixed-script and numeric inputs (Hasan et al., 5 Jan 2026).
• District Guided Tokens (DGT): Byte-level ByT5 and other transform models are prepended with region tokens, conditioning the model on dialect and systematically improving accuracy for regional variants (Islam et al., 2024).

Training Regimes and Objectives

• Loss function: token-level cross-entropy, e.g.,

$$\mathcal{L} = -\frac{1}{N}\sum_{n=1}^{N} \sum_{t=1}^{T_n} y_{n,t}\;\log\hat{y}_{n,t}$$

• Optimization techniques: AdamW, RMSProp; moderate batch sizes (4–64); 10–50 epochs.
• Data splits: typically 90/10 or 99/1 train/validation, preserving high OOV rates (Islam et al., 2024, Hasan et al., 2023).
• Hardware: commodity GPUs (e.g., Tesla T4, 12hr total training for ByT5).

Handlings of numerals, punctuation, foreign substrings, and out-of-vocabulary words are modularized, not left to pure end-to-end learning.

4. Datasets and Evaluation Metrics

Principal Datasets

• DataVerse Challenge (ITVerse 2023): 21,999 Bangla-word–IPA pairs, expanded to 37,807 unique word pairs post-processing. Test split contains 27,228 previously unseen words (Hasan et al., 2023).
• DUAL-IPA: 150,000 sentences (33% news, 66% literature), annotated by linguists; standard Bengali plus six dialectal regions; ~130k unique train words, ~35k OOV test words (Fatema et al., 2024, Hasan et al., 5 Jan 2026).
• RegIPA/Bhashamul: 35k train / 9k test sentences spanning six districts; nearly 47% OOV rate in test set (Islam et al., 2024).

Metrics

• Word Error Rate (WER):

$\mathrm{WER} = \frac{S + D + I}{N}$

where $S$ = substitutions, $D$ = deletions, $I$ = insertions, $N$ = total reference words.

Performance is reported both overall and by dialect/region, as well as for ablation variants (e.g., DGT/no-DGT).

5. Empirical Performance and Comparative Results

Key systems and their results include:

Model	Chittagong	Kishoreganj	Narail	Narsingdi	Standard	Rangpur	Tangail	Mean/Overall WER (%)	Dataset
mT5	27.8	39.7	60.4	67.1	43.4	106.4	88.1	53.5	DUAL-IPA
umT5	31.6	22.8	19.5	28.5	28.6	29.0	27.8	27.4	DUAL-IPA
BanglaIPA	12.4	12.3	10.8	14.3	10.8	10.7	11.1	11.4	DUAL-IPA
ByT5 (DGT)	--	--	--	--	--	--	--	2.07	RegIPA
ByT5 (no DGT)	--	--	--	--	--	--	--	3.45	RegIPA

• BanglaIPA achieves a mean WER of 11.4% across standard and dialectal Bengali, markedly outperforming MT5 and UMT5 by 58–79% (Hasan et al., 5 Jan 2026).
• DGT-based models (ByT5) reach WER as low as 2.07%, attributed to robust handling of OOV tokens and explicit dialectal context (Islam et al., 2024).
• Lightweight Transformers with word-caching and manual alignment achieve WER ≈ 0.106 on heavily OOV evaluation (Hasan et al., 2023).
• Contextual rewriting and STAT alignment avoid error cascades for numerals and mixed-script inputs.

Empirical evidence indicates that architecture must explicitly encode both context (for numerals and dialect) and leverage efficient segmenting/caching for performance and scale.

6. Methodological Innovations and Challenges

Notable methodological contributions:

• Contextual Numeral Rewriting: Numeral tokens are expanded to their context-specific word forms before IPA mapping, using small LLMs; this outperforms naïve word-form mapping or dropping numerals (Hasan et al., 5 Jan 2026).
• State Alignment (STAT): Passes through non-Bengali substrings; only native substrings require neural decoding, improving efficiency and robustness.
• District/Region Tokens (DGT): Conditioning the model on explicit dialectal identity narrows hypothesis space and enables fine-grained regional phonetic distinctions (Islam et al., 2024).
• Byte-level Modeling: ByT5 and similar models excel with OOV tokens and variable orthography present in regional literature.

Ongoing challenges include allophonic/coarticulatory effects, handling rare loanwords, systematic treatment of numerals and abbreviations, and out-of-distribution generalization for new dialects.

7. Applications, Limitations, and Prospects

BanglaIPA systems underpin fundamental tasks in speech technology (TTS, ASR), linguistic annotation, education, and dialectology. The robust standardization of IPA transcription for Bengali, including regional variants and numeral contexts, enables scalable resource creation and improved downstream performance in low-resource and regionalized settings.

Current limitations include:

• Partial dialect coverage and limited handling of highly novel pronunciations or proper nouns.
• External API dependence (LLM numeral rewriter) introduces system latency and infrastructure constraints.
• Performance on zero-shot dialects or unseen loanword types may degrade without explicit adaptation (Hasan et al., 5 Jan 2026, Islam et al., 2024).

Anticipated future directions involve unsupervised expansion to new dialects and lexica, replacement of LLM modules with efficient local models, integration of joint language–phoneme models, and cross-lingual transfer leveraging the Indo-Aryan language family.

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References:

• "BanglaIPA: Towards Robust Text-to-IPA Transcription with Contextual Rewriting in Bengali" (Hasan et al., 5 Jan 2026)
• "Character-Level Bangla Text-to-IPA Transcription Using Transformer Architecture with Sequence Alignment" (Hasan et al., 2023)
• "Transcribing Bengali Text with Regional Dialects to IPA using District Guided Tokens" (Islam et al., 2024)
• "IPA Transcription of Bengali Texts" (Fatema et al., 2024)