Design and Development of Speech Corpora for Air Traffic Control Training (L18-1)
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| Challenge: | The current state-of-the-art training procedures involve retired pilots that train as virtual plane pilots and process the spoken prompts to form that can be entered into software that simulates the plane movement on the radar screen. |
| Approach: | They describe the process of creating domain-specific speech corpora containing air traffic control (ATC) communication prompts. |
| Outcome: | The proposed system could be used for training air traffic controllers in the Czech Republic. |
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Estelle Delpech, Marion Laignelet, Christophe Pimm, Céline Raynal, Michal Trzos, Alexandre Arnold, Dominique Pronto
| Challenge: | AIRBUS-ATC corpus is a real-life, french-accented speech corpus of air traffic control (ATC) communications . it is composed of 59 hours of transcribed English audio, along with linguistic and meta-data annotations. |
| Approach: | They propose to use a real-life, French-accented speech corpus of ATC communications to build a robust ATC speech recognition engine. |
| Outcome: | The AIRBUS-ATC corpus is composed of 59 hours of transcribed English audio, along with linguistic and meta-data annotations. |
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool (2022.acl-srw)
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| Challenge: | Automated speech recognition (ASR) models are based on a corpus of audio recordings, but are often small or nonexistent for less common languages and dialects. |
| Approach: | This research proposal will develop a semi-automatic acoustic features extraction system that integrates phonetic transcripts with pronunciation dictionaries. |
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An Application for Building a Polish Telephone Speech Corpus (L18-1)
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| Challenge: | Specifically, we describe a tool designed to improve our Automatic Speech Recognition system performance. |
| Approach: | They propose to build a tool for speech corpus collection of a specific domain content. |
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SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale (2022.emnlp-industry)
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Raphael Tang, Karun Kumar, Gefei Yang, Akshat Pandey, Yajie Mao, Vladislav Belyaev, Madhuri Emmadi, Craig Murray, Ferhan Ture, Jimmy Lin
| Challenge: | End-to-end automatic speech recognition systems require thousands of hours of manual annotation and heavyweight computation to perform inference. |
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On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | Using a set of metrics to evaluate the learned representations, we aim to create a system that learns from natural interactions as infants learn their first language. |
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Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey (2025.emnlp-main)
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| Challenge: | Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes. |
| Approach: | They provide a review of controllable TTS methods from traditional control techniques to emerging approaches using natural language prompts. |
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
A Brief Survey of Textual Dialogue Corpora (2022.lrec-1)
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| Challenge: | Several dialogue corpora are available for research purposes, but they do not cover all the necessities of real-world applications. |
| Approach: | They analyze available dialogue corpora and propose possible approaches to create new ones. |
| Outcome: | The proposed corpus of human-human dialogues is based on a list of available dialogue corpora . it covers speakers, size, languages, collection, annotations, and domains . some trends are identified and possible approaches are also discussed . |
Praaline: An Open-Source System for Managing, Annotating, Visualising and Analysing Speech Corpora (P18-4)
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| Challenge: | Praaline is an open-source software system for constituting and managing spoken language and multimodal corpora. |
| Approach: | They present the latest developments of Praaline, an open-source software system for constituting and managing spoken language and multimodal corpora. |
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Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
| Outcome: | The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages. |