TED-LIUM: an Automatic Speech Recognition dedicated corpus

By Anthony Rousseau, Paul Deléglise, Yannick Estève. 2012

322 citations recorded by the source corpus.

This paper presents the corpus developed by the LIUM for Automatic Speech Recognition (ASR), based on the TED Talks. This corpus was built during the IWSLT 2011 Evaluation Campaign, and is composed of 118 hours of speech with its accompanying automatically aligned transcripts. We describe the content of the corpus, how the data was collected and processed, how it will be publicly available and how we built an ASR system using this data leading to a WER score of 17.4 %. The official results we obtained at the IWSLT 2011 evaluation campaign are also discussed.

Subfields: Artificial Intelligence, Evaluation & Benchmarks, Speech & Audio

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Citations in the corpus

MAESTRO: Matched Speech Text Representations through Modality Matching (2022, methodology): “Our unspoken text corpus consists of the text corpora from TEDLIUM [30] and Librispeech [31].”
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition (2021, methodology): “Data: The SpeechStew [30] dataset is assembled by putting together seven public speech corpora—AMI [74], Common Voice [75], English Broadcast News,2 LibriSpeech [28], Switchboard/Fisher,3 TED-LIUM v3 [76], [77] and Wall Street Journal.”
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation (2018, methodology): “To measure the recent advances realized in ASR technology, this word error rate can be compared to the 11.1% reached by such a state-of-the-art system in 2014 [10].”
A Survey on Neural Speech Synthesis (2021, neutral): “1 English TED-LIUM [289] 118 666 / English CALLHOME [31] 60 120 8 English RyanSpeech [414] 10 1 44.”
ESPnet: End-to-End Speech Processing Toolkit (2018, methodology): “With these state-of-theart end-to-end ASR techniques, ESPnet also provides a number of recipes for major ASR benchmarks including Wall Street Journal (WSJ) [18], Librispeech [19], TED-LIUM [20], Corpus of Spontaneous Japanese (CSJ) [21], AMI [22], HKUST Mandarin CTS [23], VoxForge [24], CHiME-4/5 [2”
The CAPIO 2017 Conversational Speech Recognition System (2017, methodology): “To see how well our approach can be generalized to nontelephony environments, we have trained separate systems using the TED-LIUM release 2 [39, 40] and LibriSpeech [41] data.”
Multilingual Speech Recognition with Corpus Relatedness Sampling (2019, background): “English TED (ted) [22] broadcast 100,000 Mandarin Hkust (hk) [23] telephone 100,000 English Switchboard (swbd)[24] telephone 100,000 Mandarin SLR18 (s18) [25] read 13,388 English Librispeech (libri) [26] read 100,000 Mandarin LDC98S73 (hub) broadcast 35,999 English Fisher (fisher) [27] telephone 100”
Densely Connected Networks for Conversational Speech Recognition (2018, background): “…LDC2005T19), Switchboard-1 Release 2 (LDC97S62), CallHome American English Speech (LDC97T14), Switchboard Cellular Part 1 (LDC2001T14), TED-LIUM [27], British Academic Spoken English (BASE) [28], Michigan Corpus of American Spoken English (MICASE) [29] and English Gigaword (LDC2003T05).”
Speaker Adaptive Training of Deep Neural Network Acoustic Models Using I-Vectors (2015, methodology): “1) Dataset: Our experiments use the benchmark TEDLIUM dataset [48] which was released to advance ASR on TED talks.”
Universal Phone Recognition with a Multilingual Allophone System (2020, background): “English voxforge, Tedlium [17], Switchboard [18] 1148k Japanese Japanese CSJ [19] 440k Mandarin Hkust [20], openSLR [21, 22] 377k Tagalog IARPA-babel106b-v0.”
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks (2019, methodology): “Other datasets, such as Mozilla’s Common Voice [Mozilla, 2019], LibriSpeech [Panayotov et al., 2015], the Spoken Wikipedia Corpora [Köhn et al., 2016], and TED-LIUM [Rousseau et al., 2012] are also publicly available.”
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages (2023, methodology): “The SpeechStew [2] dataset is assembled by putting together seven public speech corpora—AMI [78], Common Voice [79], English Broadcast News3, LibriSpeech [80], Switchboard/Fisher4, TED-LIUM v3 [81, 82] and Wall Street Journal5, which are all standard benchmarks [83–85] covering different domains in ”
Semantic Mask for Transformer based End-to-End Speech Recognition (2019, methodology): “To verify the generalization of the semantic mask, we further conduct experiments on TedLium2 [23] dataset, which is extracted from TED talks.”