Papers with transcription
Cross-Lingual Transfer Learning for Speech Translation (2025.naacl-short)
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| Challenge: | Increasing interest in building multilingual foundation models for NLP and speech research has led to limited data collection for training ST systems. |
| Approach: | They propose to use Whisper to explore the behavior of multilingual speech foundation models with restricted data. |
| Outcome: | The proposed model can translate to Chinese with a single language, and it can perform transcriptions in other languages. |
A Corpus of Spontaneous L2 English Speech for Real-situation Speaking Assessment (2024.lrec-main)
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| Challenge: | Existing automated scoring systems rely on highly controlled elicitation protocols, such as reading aloud isolated words or short sentences, limiting their ability to evaluate spontaneous speech. |
| Approach: | They propose to collect a corpus of spontaneous L2 English speech from university students as part of a French national certificate in English. |
| Outcome: | The results show that only 35.4% of the 6,350 targeted words had stress detected on the expected syllable, revealing a common stress shift to the final s. |
Learning From Failure: Data Capture in an Australian Aboriginal Community (2022.acl-long)
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| Challenge: | a prototype of a language data capture app for speakers was tested in an Aboriginal community . elicitation of word lists, phrases, etc. has been used for decades to collect data for Indigenous languages . many software tools are developed to support linguists' work . |
| Approach: | They propose to deploy an app for speakers to confirm system guesses in an approach to transcription based on word spotting. |
| Outcome: | The proposed app was tested in an Aboriginal community in australia . it was able to confirm system guesses without a transcription bottleneck . the results were compared with other apps in the community . |
Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents (2026.findings-acl)
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| Challenge: | Recent multimodal models are trained on large collections of audio-text pairs using contrastive learning or nexttoken prediction objectives. |
| Approach: | They evaluate audio language models across five semantic and paralinguistic reasoning tasks: entailment, consistency, plausibility, accent drift, and accent restraint. |
| Outcome: | The evaluations assess models across five tasks including entailment, consistency, plausibility, accent drift, and accent restraint. |
ArzEn: A Speech Corpus for Code-switched Egyptian Arabic-English (2020.lrec-1)
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| Challenge: | a corpus of Arabic-English code-switching (CS) spontaneous speech is collected in an Egyptian university soundproof room . the language in Egypt is rather complex and poses many challenges to natural language processing (NLP) |
| Approach: | They present an Egyptian Arabic-English code-switching (CS) spontaneous speech corpus. |
| Outcome: | The proposed corpus is designed to be used in automatic speech recognition systems . it provides a useful resource for analyzing the CS phenomenon from linguistic, sociological, and psychological perspectives. |
Analyzing the Quality of Counseling Conversations: the Tell-Tale Signs of High-quality Counseling (L18-1)
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| Challenge: | Behavioral and mental health disorders are the most costly and prevalent conditions worldwide. |
| Approach: | They propose to use a dataset to analyze counseling interactions by using aspects such as mirroring, empathy, and reflective listening to build text-based classifiers. |
| Outcome: | The proposed dataset can be used to build text-based classifiers able to predict the overall quality of a counseling conversation and provide insights into the linguistic differences between low-quality and high-quality counseling. |
Phonotomizer: A Compact, Unsupervised, Online Training Approach to Real-Time, Multilingual Phonetic Segmentation (2025.acl-long)
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| Challenge: | Existing approaches to phonetic segmentation are hierarchical and end-to-end . many mistakes in final output stem from subtle segmenter perturbations . |
| Approach: | They propose a phonetic segmentation system that trains on raw sound files alone . it can modulate computational exactness and reduce acoustic model size, they argue . |
| Outcome: | The proposed method reduces the size of the acoustic model and training epochs. |
Manually Annotated Corpus of Polish Texts Published between 1830 and 1918 (L18-1)
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| Challenge: | a paper presents a manually annotated corpus of 625,000 tokens of Polish texts . the corpus provides three layers: transliteration, transcription and morphosyntactic annotation. |
| Approach: | The paper presents a manually annotated large historical corpus of Polish . the corpus provides three layers: transliteration, transcription and morphosyntactic annotation. |
| Outcome: | The corpus provides three layers: transliteration, transcription and morphosyntactic annotation. |
VAST: A Corpus of Video Annotation for Speech Technologies (L18-1)
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| Challenge: | The video annotation for speech technologies corpus contains 2900 hours of video data . the data are intended to support speech technology development . |
| Approach: | The Video Annotation for Speech Technologies corpus contains 2900 hours of video data . the data are intended to support speech technology development . |
| Outcome: | The video annotation for speech technologies corpus contains 2900 hours of video data . the data are intended to support speech detection, language identification, speaker identification, and speech recognition . |
Augmented Prompt Selection for Evaluation of Spontaneous Speech Synthesis (2020.lrec-1)
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| Challenge: | Spontaneous speech is unscripted and created on the fly by the speaker, whereas read speech is pre-planned. |
| Approach: | They propose a tool that allows developers to select a varied, representative set of utterances from a spoken genre to be used for evaluation of TTS for a given domain. |
| Outcome: | The proposed tool can be used to evaluate TTS for a given domain using visualisation and tree-based algorithm. |
Development and Evaluation of Speech Synthesis Corpora for Latvian (2020.lrec-1)
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| Challenge: | Recent advances in neural speech synthesis have enabled the development of text to speech systems for all languages. |
| Approach: | They propose to obtain a suitable corpus from unannotated Latvian audio recordings using automated speech recognition and speaker segmentation and identification. |
| Outcome: | The proposed method and software tools are applied and evaluated on a Latvian public radio archive data. |
Visual-Aware Speech Recognition for Noisy Scenarios (2025.emnlp-main)
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| Challenge: | Existing audio-only models that use visual cues for transcription struggle in noisy environments. |
| Approach: | They propose a method that correlates visual cues with noise sources to improve transcription by filtering speech from noise and predicting noise labels in video inputs. |
| Outcome: | The proposed model improves transcription by correlating noise sources to visual cues in audio inputs. |
Linguistically Motivated Sign Language Segmentation (2023.findings-emnlp)
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| Challenge: | Sign language segmentation is a crucial task in sign language processing systems. |
| Approach: | They propose to combine two kinds of segmentation: segmentation into individual signs and segmentation to segment into phrases, larger units comprising several signs. |
| Outcome: | The proposed model is based on linguistic cues observed in sign language corpora and replaces the predominant IO tagging scheme with BIO taging to account for continuous signing. |
Linguistic Survey of India and Polyglotta Africana: Two Retrostandardized Digital Editions of Large Historical Collections of Multilingual Wordlists (2024.lrec-main)
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| Challenge: | Linguistic Survey of India and Polyglotta Africana are two of the largest historical collections of multilingual wordlists. |
| Approach: | They present a retro-standardized edition of the Linguistic Survey of India and the Polyglotta Africana, which are two of the largest historical collections of multilingual wordlists. |
| Outcome: | The LSI and PA are the largest historical collections of multilingual wordlists . but no editions in which the original data is presented in standardized form have been produced so far . |
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR (2024.emnlp-main)
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Shashi Kumar, Srikanth Madikeri, Juan Pablo Zuluaga Gomez, Iuliia Thorbecke, Esaú Villatoro-tello, Sergio Burdisso, Petr Motlicek, Karthik S, Aravind Ganapathiraju
| Challenge: | Existing approaches to automatic speech recognition use cascaded pipelines for tasks like voice activity detection, diarization, transcription and subsequent processing. |
| Approach: | They propose a single Transducer-based model that integrates task-specific tokens into the reference text during ASR model training, streamlining inference and eliminating the need for separate NLP models. |
| Outcome: | The proposed model outperforms the existing pipeline on speaker change detection, endpointing, and NER tasks while outperforming the existing model in individual task performance. |
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
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Bashar Talafha, Karima Kadaoui, Samar Magdy, Mariem Habiboullah, Chafei Chafei, Ahmed El-Shangiti, Hiba Zayed, Mohamedou Tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El Moatez Billah Nagoudi, Benelhadj Saadia, Hamzah Alsayadi, Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-chammakhy, Amal Makouar, Yousra Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, Muhammad Abdul-Mageed
| Challenge: | despite recent advances in speech processing, the majority of world languages and dialects remain uncovered. |
| Approach: | They propose to collect and transcribe a new Arabic dataset for eight dialects . they also develop strong baselines exploiting the new dataset . |
| Outcome: | The proposed dataset covers eight Arabic dialects, including Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni. |