Linear Semantic Segmentation for Low-Resource Spoken Dialects

• URL: http://arxiv.org/abs/2605.06276v1
• Date: Thu, 07 May 2026 13:51:24 GMT
• Title: Linear Semantic Segmentation for Low-Resource Spoken Dialects
• Abstract summary: We introduce a new multi-genre benchmark for semantic segmentation in conversational Arabic.<n>We show that segmentation models performing well on MSA news genres degrade on dialectal transcribed speech.<n>We propose a segmentation model that targets local semantic coherence and robustness to discourse discontinuities.
• Score: 6.6238001631960275
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic segmentation is a core component of discourse analysis, yet existing models are primarily developed and evaluated on high-resource written text, limiting their effectiveness on low-resource spoken varieties. In particular, dialectal Arabic exhibits informal syntax, code-switching, and weakly marked discourse structure that challenge standard segmentation approaches. In this paper, we introduce a new multi-genre benchmark (more than 1000 samples) for semantic segmentation in conversational Arabic, focusing on dialectal discourse. The benchmark covers transcribed casual telephone conversations, code-switched podcasts, broadcast news, and expressive dialogue from novels, and was annotated and validated by native Arabic annotators. Using this benchmark, we show that segmentation models performing well on MSA news genres degrade on dialectal transcribed speech. We further propose a segmentation model that targets local semantic coherence and robustness to discourse discontinuities, consistently outperforming strong baselines on dialectal non-news genres. The benchmark and approach generalize to other low-resource spoken languages.

Related papers

• Quantifying the Generation Modality Gap in Speech-Text Language Models [22.8588553114451]
  Pure speech language models often lag behind text and speech-text language models in generating coherent content.<n>We study the speech-text modality gap in a family of spoken language models, based on flow matching for continuous acoustic feature generation.<n>We construct a unified generation-based evaluation suite that compares speech-only, text-only, and speech-text language models trained on matched data.
  arXiv  Detail & Related papers  (2026-09-13T19:09:07Z)
• Bulbul: A Dataset for Dialectal Arabic Speech Recognition [23.17840151160424]
  We present BULBUL, a multi-dialect Arabic ASR dataset collected from 275 speakers in 11 Arab countries.<n>BULBUL includes structured dialect and sub-dialect coverage, as well as recordings of classical Arabic and modern standard Arabic spoken by participants in their native dialectal accents.
  arXiv  Detail & Related papers  (2026-08-22T13:27:12Z)
• MUSCAT: MUltilingual, SCientific ConversATion Benchmark [60.87925076316812]
  The goal of multilingual speech technology is to facilitate seamless communication between individuals speaking different languages.<n>To create this experience, speech technology needs to address several challenges: Handling mixed multilingual input, specific vocabulary, and code-switching.<n>We propose a new benchmark to evaluate current Automatic Speech Recognition (ASR) systems, whether they are able to handle these challenges.
  arXiv  Detail & Related papers  (2026-04-17T10:39:01Z)
• Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology [25.96097632833693]
  Dialectal Arabic (DA) speech data vary widely in domain coverage, dialect labeling practices, and recording conditions.<n>We conduct a computational analysis of linguistic dialectness'' alongside objective proxies of audio quality on the training splits of widely used DA corpora.<n>We find substantial heterogeneity both in acoustic conditions and in the strength and consistency of dialectal signals across datasets.
  arXiv  Detail & Related papers  (2026-01-19T19:02:40Z)
• On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation [88.77441715819366]
  Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content.<n>We propose a variety of likelihood- and generative-based evaluation methods that serve in place of naive global token perplexity.
  arXiv  Detail & Related papers  (2026-01-09T22:01:56Z)
• WESR: Scaling and Evaluating Word-level Event-Speech Recognition [59.21814194620928]
  Speech conveys not only linguistic information but also rich non-verbal vocal events such as laughing and crying.<n>We develop a refined taxonomy of 21 vocal events, with a new categorization into discrete (standalone) versus continuous (mixed with speech) types.<n>Based on the refined taxonomy, we introduce WESR-Bench, an expert-annotated evaluation set (900+ utterances) with a novel position-aware protocol.
  arXiv  Detail & Related papers  (2026-01-08T02:23:21Z)
• The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness [10.837144343838945]
  Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories.<n>We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects.
  arXiv  Detail & Related papers  (2025-08-24T13:06:00Z)
• Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic Embeddings [2.615008111842321]
  We introduce an end-to-end scheme for topic segmentation using semantic speech encoders. We propose a new benchmark for spoken news topic segmentation by utilizing a dataset featuring 1000 hours of publicly available recordings. Our results demonstrate that while the traditional pipeline approach achieves a state-of-the-art $P_k$ score of 0.2431 for English, our end-to-end model delivers a competitive $P_k$ score of 0.2564.
  arXiv  Detail & Related papers  (2024-09-10T05:24:36Z)
• Modeling Orthographic Variation in Occitan's Dialects [3.038642416291856]
  Large multilingual models minimize the need for spelling normalization during pre-processing. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing.
  arXiv  Detail & Related papers  (2024-04-30T07:33:51Z)
• Neural approaches to spoken content embedding [1.3706331473063877]
  We contribute new discriminative acoustic word embedding (AWE) and acoustically grounded word embedding (AGWE) approaches based on recurrent neural networks (RNNs) We apply our embedding models, both monolingual and multilingual, to the downstream tasks of query-by-example speech search and automatic speech recognition.
  arXiv  Detail & Related papers  (2023-08-28T21:16:08Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• Bridging the Modality Gap for Speech-to-Text Translation [57.47099674461832]
  End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously. We propose a Speech-to-Text Adaptation for Speech Translation model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text.
  arXiv  Detail & Related papers  (2020-10-28T12:33:04Z)

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.