arXiv:2603. 26292v2 Announce Type: replace-cross Abstract: Syllable-level units offer compact and linguistically meaningful representations for spoken language modeling and unsupervised word discovery, but research on syllabification remains fragmented across disparate implementations, datasets, and evaluation protocols.
arXiv:2608. 01935v1 Announce Type: cross Abstract: Prior work in Ancient Greek NLP relies on corpora that do not disambiguate the phonemic vowel length of alpha, iota, and ypsilon, together known as the dichrona.
DiscoPhon is a multilingual benchmark designed to evaluate unsupervised phoneme discovery from discrete speech units. It includes 6 development and 6 test languages that cover a wide range of phonemic contrasts, and requires systems to generate discrete units mapped to a predefined phoneme inventory using only 10 hours of speech from an unseen language. The benchmark assesses unit quality, recognition, and segmentation, and provides four pretrained multilingual HuBERT and SpidR baselines that demonstrate current models can produce units that correlate well with phonemes, though performance varies across languages.
arXiv:2609. 21362v1 Announce Type: new Abstract: Conventional tokenizers represent text as characters or statistically derived subwords, overlooking the internal phonological structure of syllables and often requiring large vocabularies.
arXiv:2607. 04064v1 Announce Type: cross Abstract: Unsupervised syllabic tokenization aims to learn discrete syllabic tokens that capture latent linguistic content-related structure from raw speech.
arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. L...
The paper introduces a training‑free speech‑and‑text‑to‑pronunciation (ST2P) pipeline that combines lexical candidates from G2P tools with acoustic rescoring using frozen pretrained S2P models. By performing a left‑to‑right greedy search over whole‑sequence negative log‑likelihoods, the method achieves a dramatic reduction in character error rate on Japanese corpora, outperforming both baseline G2P/S2P approaches and commercial multimodal LLMs. The approach is also significantly faster—3–3.5× faster than beam search and twice as fast as direct decoding—while maintaining high accuracy across multiple languages.
We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. Large end-to-end Transformer-based ASR models such...
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, using a discriminative conditional random field over a word lattice built from dictionaries. It addresses data scarcity by generating over two million sentences with large language models. Experiments show the method surpasses traditional morphological analyzers and neural sequence models, achieving 99.62% target word reading accuracy and very low phoneme error rates on the Joyo‑Kanji‑Yomi benchmark.
The paper introduces a token‑level extension of Omni‑Temporal Classification (OTC) for automatic speech recognition, allowing unsupported tokens to be bypassed while preserving supervision for the rest of the word. Across 19 languages and three corpora, this token‑level OTC consistently outperforms standard CTC, achieving the lowest mean word error rate on every dataset and a 9.45% average relative WER reduction. A predictive‑entropy‑indexed schedule replaces epoch‑based relaxation, reducing training‑length dependence while maintaining performance.
The paper introduces a new strategy for connecting large language models (LLMs) to speech encoders in automatic speech recognition (ASR) systems by sharing a single connector across languages within the same linguistic family. This approach reduces the number of parameters needed compared to training a separate connector for each language, while improving generalization across different domains and real‑world corpora. Experiments with two multilingual LLMs and two speech datasets demonstrate that family‑based connectors are both efficient and effective for multilingual ASR deployment.
State-of-the-art speech datasets predominantly focus on widely spoken languages, often overlooking low-resource languages such as Luxembourgish, which remain underrepresented in speech technology research. In this work, we introduce LuxEmo, a 21-hour conversational expressive speech corpus for Luxembourgish with 4 emotion categories.
arXiv:2510.10774v4 Announce Type: replace-cross Abstract: Persian remains substantially underrepresented in open speech-text resources, limiting progress in multi-speaker text-to-speech (TTS), speech...