Phonetic Modeling of Dialectal Variation in Vietnamese Speech

• URL: http://arxiv.org/abs/2605.24451v1
• Date: Sat, 23 May 2026 08:00:26 GMT
• Title: Phonetic Modeling of Dialectal Variation in Vietnamese Speech
• Abstract summary: Vietnamese exhibits substantial dialectal phonetic variation across Northern, Central, and Southern regions.<n>We propose a dialect-aware phonetic framework that explicitly models Vietnamese phonological structure and dialectal variation at both the vocabulary and decoding levels.
• Score: 8.447993256993428
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vietnamese exhibits substantial dialectal phonetic variation across Northern, Central, and Southern regions, where identical lexical items may be realized with markedly different pronunciations. Such variation poses challenges for automatic speech recognition (ASR) and remains difficult to model computationally due to the complex relationship between Vietnamese orthography and phonology. Existing approaches typically address dialect variability at the word level, assuming dialect-invariant mappings between spelling and pronunciation, which limits their ability to capture systematic phonetic differences. We propose a dialect-aware phonetic framework that explicitly models Vietnamese phonological structure and dialectal variation at both the vocabulary and decoding levels. The framework introduces a phonetic vocabulary that decomposes each syllable into structured phonetic components and maps them to dialect-specific IPA representations, together with a phonetic-structure decoder that jointly predicts these components. Experiments on the UIT-ViMD, a only-available dataset for multi-dialect in Vietnamese, show that the proposed approach outperforms various pre-trained baselines, \textbf{especially matches the performance of the strongest pretrained wav2ve2-base-vi-250h} across dialects while \textbf{using substantially fewer parameters and no external pretraining}. Code for experimental reproducibility will be publicly available upon the acceptance of this paper.

Related papers

• Probing in the Wild: A Case Study of Self-Supervised Speech Representations on Mandarin Sub-dialects with Unsupervised Articulatory Analysis [1.6274707775531156]
  We present a case study of articulatory feature representations in a Mandarin self-supervised speech model using an entirely unlabeled probing pipeline.<n>Our results reveal a structured pattern in articulatory feature decodability across Mandarin sub-dialects.<n>These findings suggest that language-agnostic articulatory probing can be applied to real-world dialect corpora.
  arXiv  Detail & Related papers  (2026-06-24T06:39:28Z)
• Syllabic-Structure Decoder for Automatic Speech Recognition in Vietnamese [8.447993256993428]
  We are motivated from the phonemic features of Vietnamese to propose a Syllabic-Structure Decoder for ASR.<n>Our approach explicitly captures the phonological composition of syllables, enabling the decoder to generate valid syllabic structures from a compact phonemic inventory.
  arXiv  Detail & Related papers  (2026-05-27T02:51:09Z)
• Speech Codec Probing from Semantic and Phonetic Perspectives [49.01048570474675]
  Speech tokenizers are essential for connecting speech to large language models (LLMs) in multimodal systems.<n> emerging evidence suggests that what is termed "semantic" in speech representations does not align with text-derived semantics.
  arXiv  Detail & Related papers  (2026-03-11T03:32:25Z)
• DiaMoE-TTS: A Unified IPA-Based Dialect TTS Framework with Mixture-of-Experts and Parameter-Efficient Zero-Shot Adaptation [14.224017622788061]
  We present DiaMoE-TTS, a unified IPA-based framework that standardizes phonetic representations and resolves grapheme-to-phoneme ambiguities.<n>Unlike approaches dependent on large-scale or proprietary resources, DiaMoE-TTS enables scalable, open-data-driven synthesis.
  arXiv  Detail & Related papers  (2025-09-25T07:02:04Z)
• TSPC: A Two-Stage Phoneme-Centric Architecture for code-switching Vietnamese-English Speech Recognition [0.855801641444342]
  Code-switching (CS) presents a significant challenge for general Auto-Speech Recognition (ASR) systems.<n>Existing methods often fail to capture the subtle phonological shifts inherent in CS scenarios.<n>We propose a novel architecture for Vietnamese-English CS ASR, a Two-Stage Phoneme-Centric model (TSPC)
  arXiv  Detail & Related papers  (2025-09-07T09:19:03Z)
• Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically [58.019484208091534]
  Cross-lingual alignment in pretrained language models (LMs) has enabled efficient transfer in text-based LMs.<n>It remains an open question whether findings and methods from text-based cross-lingual alignment apply to speech.
  arXiv  Detail & Related papers  (2025-05-26T07:21:20Z)
• The realization of tones in spontaneous spoken Taiwan Mandarin: a corpus-based survey and theory-driven computational modeling [1.7723990552388866]
  This study investigates the tonal realization of Mandarin disyllabic words with all 20 possible combinations of two tones.<n>The results show that meaning in context and phonetic realization are far more entangled than standard linguistic theory predicts.
  arXiv  Detail & Related papers  (2025-03-29T17:39:55Z)
• Multilingual context-based pronunciation learning for Text-to-Speech [13.941800219395757]
  Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end. We showcase a multilingual unified front-end system that addresses any pronunciation related task, typically handled by separate modules. We find that the multilingual model is competitive across languages and tasks, however, some trade-offs exists when compared to equivalent monolingual solutions.
  arXiv  Detail & Related papers  (2023-07-31T14:29:06Z)
• DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules [64.93179829965072]
  DADA is a modular approach to imbue SAE-trained models with multi-dialectal robustness. We show that DADA is effective for both single task and instruction fine language models.
  arXiv  Detail & Related papers  (2023-05-22T18:43:31Z)
• Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech [88.22544315633687]
  Polyphone disambiguation aims to capture accurate pronunciation knowledge from natural text sequences for reliable Text-to-speech systems. We propose Dict-TTS, a semantic-aware generative text-to-speech model with an online website dictionary. Experimental results in three languages show that our model outperforms several strong baseline models in terms of pronunciation accuracy.
  arXiv  Detail & Related papers  (2022-06-05T10:50:34Z)
• A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition [80.87085897419982]
  We propose a novel acoustic modeling technique for accurate multi-dialect speech recognition with a single AM. Our proposed AM is dynamically adapted based on both dialect information and its internal representation, which results in a highly adaptive AM for handling multiple dialects simultaneously. The experimental results on large scale speech datasets show that the proposed AM outperforms all the previous ones, reducing word error rates (WERs) by 8.11% relative compared to a single all-dialects AM and by 7.31% relative compared to dialect-specific AMs.
  arXiv  Detail & Related papers  (2022-05-06T06:07:09Z)
• Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties [77.2347265289855]
  We focus on phoneme recognition using Allosaurus, a method for multilingual recognition based on phonetic annotation. To evaluate in a challenging real-world scenario, we curate phone recognition datasets for Bukusu and Saamia, two varieties of the Luhya language cluster of western Kenya and eastern Uganda. We find that fine-tuning of Allosaurus, even with just 100 utterances, leads to significant improvements in phone error rates.
  arXiv  Detail & Related papers  (2021-04-04T15:07:55Z)
• Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text [69.55642178336953]
  We present an approach to unsupervised audio representation learning. Based on a triplet neural network architecture, we harnesses semantically related cross-modal information to estimate audio track-relatedness. We show that our approach is invariant to the variety of annotation styles as well as to the different languages of this collection.
  arXiv  Detail & Related papers  (2020-03-27T07:37:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.