Challenge: Existing work in the area of radio browsing using automatic speech recognition (ASR) has been done by the United Nations in Uganda, and Keyword Spotting systems in Somalia.
Approach: They propose to use a Luganda radio speech corpus of 155 hours to build a usable radio monitoring automatic speech recognition system.
Outcome: The makerere artificial intelligence lab releases a Luganda radio speech corpus of 155 hours.

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CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)

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Challenge: Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications.
Approach: They propose to use public speech corpora to evaluate the quality of automatic speech recognition (ASR) they calculate an average Word Error Rate (WER) per corpus, per system and per corpor-system pair .
Outcome: The proposed corpus evaluates the quality of automatic speech recognition systems using public speech corpora and transcripts generated by state-of-the-art systems.
BembaSpeech: A Speech Recognition Corpus for the Bemba Language (2022.lrec-1)

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Challenge: Existing speech recognition systems for African languages are very low . lack of resources (speech and text) can be attributed to poor quality of speech.
Approach: They present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting of 24 hours of read speech in the Bemba language.
Outcome: The proposed model achieves a word error rate (WER) of 32.91% on the Bemba language . the 1 billion XLS-R parameter model achieve better performance than the monolingual pre-trained English model on the corpus.
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)

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Challenge: Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible.
Approach: They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries.
Outcome: The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country.
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition (2020.lrec-1)

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Challenge: Romanian language is under-resourced due to the lack of acoustic and linguistic resources.
Approach: They propose to use a Romanian speech corpus to train automatic speech recognition algorithms based on the spoken hotword detection mechanism.
Outcome: The read speech corpus is a speech recognition system that can perform automatic speech recognition and speech synthesis using state-of-the-art speech recognition toolkit.
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic Information (2024.lrec-main)

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Challenge: Existing datasets for automatic speech recognition (ASR) in the endangered Kichwa language have been limited.
Approach: They present Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador.
Outcome: The proposed dataset shows that it can be used to build an automatic speech recognition system for the endangered language with reliable quality despite its small size.
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.
Outcome: The proposed system will be used to improve language recognition and model feedback in less common languages and dialects.
ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus (2024.lrec-main)

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Challenge: rynSpeech corpus is a dataset that can be used for both Text-to-Speecher (TTS) and Automatic Speech Recognition (ASR) speakers of many African languages have no access to voice-enabled applications in their native languages.
Approach: They propose a dataset to collect Yorùbá speech data that can be used for both TTS and ASR tasks.
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Large Vocabulary Read Speech Corpora for Four Ethiopian Languages: Amharic, Tigrigna, Oromo and Wolaytta (2020.lrec-1)

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Challenge: Automatic Speech Recognition (ASR) is one of the most important technologies to support spoken communication in modern life.
Approach: They have developed four large speech corpora for four Ethiopian languages . they have word error rates of 37.65%, 31.03%, 38.02%, 33.89% for each language .
Outcome: The proposed corpora achieve word error rates of 37.65%, 31.03%, 38.02%, 33.89% for Amharic, Tigrigna, Oromo and Wolaytta.
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu (2020.lrec-1)

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Challenge: Language documentation is a rapidly growing field due to its urgency.
Approach: They propose to use phoneme recognition to automatically recognize spoken languages and translate them to global languages.
Outcome: The proposed tool performs better than existing methods with American English, Austrian German and Slovenian as source and target languages.
Speech Recognition Corpus of the Khinalug Language for Documenting Endangered Languages (2024.lrec-main)

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Challenge: Existing tools to document endangered languages are limited due to data scarcity and the need for training.
Approach: They propose to use a speech corpus for Khinalug, an endangered language spoken in northern Azerbaijan, to create a model that can be used in language documentation scenarios.
Outcome: The proposed model achieves 6.65 CER points and 25.53 WER points in low-resource scenarios.

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