Toward Conversational Hungarian Speech Recognition: Introducing the BEA-Large and BEA-Dialogue Datasets

• URL: http://arxiv.org/abs/2511.13529v1
• Date: Mon, 17 Nov 2025 16:02:08 GMT
• Title: Toward Conversational Hungarian Speech Recognition: Introducing the BEA-Large and BEA-Dialogue Datasets
• Authors: Máté Gedeon, Piroska Zsófia Barta, Péter Mihajlik, Tekla Etelka Gráczi, Anna Kohári, Katalin Mády,
• Abstract summary: We introduce two new datasets -- BEA-Large and BEA-Dialogue -- constructed from the previously unprocessed portions of the Hungarian speech corpus named BEA.<n> BEA-Large extends BEA-Base with 255 hours of spontaneous speech from 433 speakers, enriched with detailed segment-level metadata.<n> BEA-Dialogue, comprising 85 hours of spontaneous conversations, is a Hungarian speech corpus featuring natural dialogues partitioned into speaker-independent subsets.
• Score: 1.3575563075756973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancement of automatic speech recognition (ASR) has been largely enhanced by extensive datasets in high-resource languages, while languages such as Hungarian remain underrepresented due to limited spontaneous and conversational corpora. To address this gap, we introduce two new datasets -- BEA-Large and BEA-Dialogue -- constructed from the previously unprocessed portions of the Hungarian speech corpus named BEA. BEA-Large extends BEA-Base with 255 hours of spontaneous speech from 433 speakers, enriched with detailed segment-level metadata. BEA-Dialogue, comprising 85 hours of spontaneous conversations, is a Hungarian speech corpus featuring natural dialogues partitioned into speaker-independent subsets, supporting research in conversational ASR and speaker diarization. We establish reproducible baselines on these datasets using publicly available ASR models, with the fine-tuned Fast Conformer model achieving word error rates as low as 14.18\% on spontaneous and 4.8\% on repeated speech. Diarization experiments yield diarization error rates between 13.05\% and 18.26\%, providing reference points for future improvements. The results highlight the persistent difficulty of conversational ASR, particularly due to disfluencies, overlaps, and informal speech patterns. By releasing these datasets and baselines, we aim to advance Hungarian speech technology and offer a methodological framework for developing spontaneous and conversational benchmarks in other languages.

Related papers

• Speaker-Aware Simulation Improves Conversational Speech Recognition [1.0251581485267474]
  We adapt and implement the SASC framework for Hungarian conversational ASR.<n>We propose C-SASC, an extended variant that incorporates pause modeling conditioned on utterance duration.<n>We generate synthetic Hungarian dialogues from the BEA-Large corpus and combine them with real conversational data for ASR training.
  arXiv  Detail & Related papers  (2026-02-04T17:12:09Z)
• Multi-Level Embedding Conformer Framework for Bengali Automatic Speech Recognition [2.235406148098187]
  This research presents an end-to-end framework for Bengali ASR.<n>It builds on a Conformer-CTC backbone with a multi-level embedding fusion mechanism.<n>The model captures fine-grained phonetic cues and higher-level contextual patterns.
  arXiv  Detail & Related papers  (2025-12-23T04:39:12Z)
• MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Models [59.80042864360884]
  Speaker-attributed automatic speech recognition (SA-ASR) aims to transcribe speech while assigning transcripts to the corresponding speakers accurately.<n>This paper introduces a novel approach, leveraging a frozen multilingual ASR model to incorporate speaker attribution into the transcriptions.
  arXiv  Detail & Related papers  (2024-11-27T09:01:08Z)
• Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition [48.527630771422935]
  We propose a synthetic data generation pipeline for multi-speaker conversational ASR. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings.
  arXiv  Detail & Related papers  (2024-08-17T14:47:05Z)
• ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation [79.66359274050885]
  We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks.
  arXiv  Detail & Related papers  (2023-05-24T07:42:15Z)
• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [70.08842857515141]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD.<n>Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• Using Kaldi for Automatic Speech Recognition of Conversational Austrian German [5.887969742827489]
  This paper presents ASR experiments with read and conversational Austrian German as target. We improve a Kaldi-based ASR system by incorporating a knowledge-based pronunciation lexicon. We achieve best WER of 0.4% on Austrian German read speech and best average WER of 48.5% on conversational speech.
  arXiv  Detail & Related papers  (2023-01-16T15:28:28Z)
• BEA-Base: A Benchmark for ASR of Spontaneous Hungarian [0.0]
  We introduce BEA-Base, a subset of the BEA spoken Hungarian database comprising mostly spontaneous speech of 140 speakers. It is built specifically to assess Automatic Speech Recognition, primarily for conversational AI applications. Results show the feasibility of using BEA-Base for training and evaluation of Hungarian speech recognition systems.
  arXiv  Detail & Related papers  (2022-02-01T17:45:22Z)
• Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition [62.94773371761236]
  We consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate. We propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. Our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER)
  arXiv  Detail & Related papers  (2021-03-12T10:10:13Z)
• Unsupervised Cross-lingual Representation Learning for Speech Recognition [63.85924123692923]
  XLSR learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations. Experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining.
  arXiv  Detail & Related papers  (2020-06-24T18:25:05Z)

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.