We introduced a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical speech and 1200h of unlabeled general-domain speech. To our best knowledge, VietMed is by far the world’s largest public medical speech recognition dataset in 7 aspects: total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents. VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration. Additionally, we are the first to present a medical ASR dataset covering all ICD-10 disease groups and all accents within a country.
1 PAPER • 2 BENCHMARKS
JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
1 PAPER • 5 BENCHMARKS
DEEP-VOICE: Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion This dataset contains examples of real human speech, and DeepFake versions of those speeches by using Retrieval-based Voice Conversion.
1 PAPER • 1 BENCHMARK
This work introduces Zambezi Voice, an open-source multilingual speech resource for Zambian languages. It contains two collections of datasets: unlabelled audio recordings of radio news and talk shows programs (160 hours) and labelled data (over 80 hours) consisting of read speech recorded from text sourced from publicly available literature books. The dataset is created for speech recognition but can be extended to multilingual speech processing research for both supervised and unsupervised learning approaches. To our knowledge, this is the first multilingual speech dataset created for Zambian languages. We exploit pretraining and cross-lingual transfer learning by finetuning the Wav2Vec2.0 large-scale multilingual pre-trained model to build end-to-end (E2E) speech recognition models for our baseline models. The dataset is released publicly under a Creative Commons BY-NC-ND 4.0 license and can be accessed through the project repository.
1 PAPER • NO BENCHMARKS YET
NusaCrowd is a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, the authors have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments.
2 PAPERS • NO BENCHMARKS YET
Corpus of Egyptian Arabic-English Code-switching (ArzEn) is a spontaneous conversational speech corpus, obtained through informal interviews held at the German University in Cairo. The participants discussed broad topics, including education, hobbies, work, and life experiences. The corpus currently contains 12 hours of speech, having 6,216 utterances. The recordings were transcribed and translated into monolingual Egyptian Arabic and monolingual English.
Open Images is a computer vision dataset covering ~9 million images with labels spanning thousands of object categories. A subset of 1.9M includes diverse annotations types.
4 PAPERS • NO BENCHMARKS YET
ESB is a benchmark for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. It comprises eight English speech recognition datasets, capturing a broad range of domains, acoustic conditions, speaker styles, and transcription requirements.
A Chinese Mandarin speech corpus by Beijing DataTang Technology Co., Ltd, containing 200 hours of speech data from 600 speakers. The transcription accuracy for each sentence is larger than 98%. Aidatatang_200zh is a free Chinese Mandarin speech corpus provided by Beijing DataTang Technology Co., Ltd under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License. The contents and the corresponding descriptions of the corpus include:
0 PAPER • NO BENCHMARKS YET
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark, with approximately 12 hours of speech supervision per language. FLEURS can be used for a variety of speech tasks, including Automatic Speech Recognition (ASR), Speech Language Identification (Speech LangID), Translation and Retrieval. In this paper, we provide baselines for the tasks based on multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable speech technology in more languages and catalyze research in low-resource speech understanding.
59 PAPERS • 1 BENCHMARK
Russian dataset of emotional speech dialogues. This dataset was assembled from ~3.5 hours of live speech by actors who voiced pre-distributed emotions in the dialogue for ~3 minutes each. <br> Each sample of dataset contains name of part from the original dataset studio source, speech file (16000 or 44100Hz) of human voice, 1 of 7 labeled emotions and the speech-to-texted part of voice speech. <br>
The EMODB database is the freely available German emotional database. The database is created by the Institute of Communication Science, Technical University, Berlin, Germany. Ten professional speakers (five males and five females) participated in data recording. The database contains a total of 535 utterances. The EMODB database comprises of seven emotions: 1) anger; 2) boredom; 3) anxiety; 4) happiness; 5) sadness; 6) disgust; and 7) neutral. The data was recorded at a 48-kHz sampling rate and then down-sampled to 16-kHz.
6 PAPERS • 1 BENCHMARK
VocalSound is a free dataset consisting of 21,024 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. The VocalSound dataset also contains meta-information such as speaker age, gender, native language, country, and health condition.
10 PAPERS • 1 BENCHMARK
This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2].
2 PAPERS • 1 BENCHMARK
The MagicData-RAMC corpus contains 180 hours of conversational speech data recorded from native speakers of Mandarin Chinese over mobile phones with a sampling rate of 16 kHz. The dialogs in the dialogs are classified into 15 diversified domains and tagged with topic labels, ranging from science and technology to ordinary life. Accurate transcription and precise speaker voice activity timestamps are manually labeled for each sample. Speakers' detailed information is also provided.
9 PAPERS • NO BENCHMARKS YET
Data collection was conducted by asking some adults from social media and some students from an elementary school to participate in our experiment. Table.1 shows the number of data gathered for recognizing each color. Due to the fact that two words are used for black in Persian, the number of black samples is more. In addition, because the color recognition is a RAN task, a sequence of data has been gathered. Table.2 depicts the number of sequence data for colors. For the meaningless words, 12 voices have been gathered on average for each word (there are 40 meaningless words in this task).
ADIMA is a novel, linguistically diverse, ethically sourced, expert annotated and well-balanced multilingual profanity detection audio dataset comprising of 11,775 audio samples in 10 Indic languages spanning 65 hours and spoken by 6,446 unique users.
The ROBIN Technical Acquisition Speech Corpus (ROBINTASC) was developed within the ROBIN project. Its main purpose was to improve the behaviour of a conversational agent, allowing human-machine interaction in the context of purchasing technical equipment. It contains over 6 hours of read speech in Romanian language. We provide text files, associated speech files (WAV, 44.1KHz, 16-bit, single channel), annotated text files in CoNLL-U format.
Spoken Language Understanding Evaluation (SLUE) is a suite of benchmark tasks for spoken language understanding evaluation. It consists of limited-size labeled training sets and corresponding evaluation sets. This resource would allow the research community to track progress, evaluate pre-trained representations for higher-level tasks, and study open questions such as the utility of pipeline versus end-to-end approaches. The first phase of the SLUE benchmark suite consists of named entity recognition (NER), sentiment analysis (SA), and ASR on the corresponding datasets.
20 PAPERS • 3 BENCHMARKS
AliMeeting corpus consists of 120 hours of recorded Mandarin meeting data, including far-field data collected by 8-channel microphone array as well as near-field data collected by headset microphone. Each meeting session is composed of 2-4 speakers with different speaker overlap ratio, recorded in rooms with different size.
37 PAPERS • 1 BENCHMARK
WenetSpeech is a multi-domain Mandarin corpus consisting of 10,000+ hours high-quality labeled speech, 2,400+ hours weakly labelled speech, and about 10,000 hours unlabeled speech, with 22,400+ hours in total. The authors collected the data from YouTube and Podcast, which covers a variety of speaking styles, scenarios, domains, topics, and noisy conditions. An optical character recognition (OCR) based method is introduced to generate the audio/text segmentation candidates for the YouTube data on its corresponding video captions.
39 PAPERS • 1 BENCHMARK
Europarl-ASR (EN) is a 1300-hour English-language speech and text corpus of parliamentary debates for (streaming) Automatic Speech Recognition training and benchmarking, speech data filtering and speech data verbatimization, based on European Parliament speeches and their official transcripts (1996-2020). Includes dev-test sets for streaming ASR benchmarking, made up of 18 hours of manually revised speeches. The availability of manual non-verbatim and verbatim transcripts for dev-test speeches makes this corpus also useful for the assessment of automatic filtering and verbatimization techniques. The corpus is released under an open licence at https://www.mllp.upv.es/europarl-asr/
5 PAPERS • 2 BENCHMARKS
The OLR 2021 dataset contains the data for the Oriental Language Recognition (OLR) 2021 Challenge, which intends to improve the performance of language recognition systems and speech recognition systems within multilingual scenarios.
The Easy Communications (EasyCom) dataset is a world-first dataset designed to help mitigate the cocktail party effect from an augmented-reality (AR) -motivated multi-sensor egocentric world view. The dataset contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head and face bounding boxes and source identification labels. We have created and are releasing this dataset to facilitate research in multi-modal AR solutions to the cocktail party problem.
15 PAPERS • 4 BENCHMARKS
CrowdSpeech is a publicly available large-scale dataset of crowdsourced audio transcriptions. It contains annotations for more than 20 hours of English speech from more than 1,000 crowd workers.
Golos is a Russian speech dataset suitable for speech research. The dataset mainly consists of recorded audio files manually annotated on the crowd-sourcing platform. The total duration of the audio is about 1240 hours.
GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised and unsupervised training.
56 PAPERS • 3 BENCHMARKS
This Sanskrit speech corpus has more than 78 hours of audio data and contains recordings of 45,953 sentences with a sampling rate of 22KHz. The content is mainly readings of texts spanning over various Śāstras of Saṃskṛtam literature and also includes contemporary stories, radio program, extempore discourse, etc.
SPGISpeech (pronounced “speegie-speech”) is a large-scale transcription dataset, freely available for academic research. SPGISpeech is a collection of 5,000 hours of professionally-transcribed financial audio. Contrary to previous transcription datasets, SPGISpeech contains global english accents, strongly varying audio quality as well as both spontaneous and presentation style speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted including sentence structure and capitalization.
13 PAPERS • 1 BENCHMARK
Timers and Such is an open source dataset of spoken English commands for common voice control use cases involving numbers. The dataset has four intents, corresponding to four common offline voice assistant uses: SetTimer, SetAlarm, SimpleMath, and UnitConversion. The semantic label for each utterance is a dictionary with the intent and a number of slots.
4 PAPERS • 1 BENCHMARK
MediaSpeech is a media speech dataset (you might have guessed this) built with the purpose of testing Automated Speech Recognition (ASR) systems performance. The dataset consists of short speech segments automatically extracted from media videos available on YouTube and manually transcribed, with some pre- and post-processing. The dataset contains 10 hours of speech for each language provided. This release contains audio datasets in French, Arabic, Turkish and Spanish, and is a part of a larger private dataset.
CSRC is a collection of data for Children Speech Recognition. The data for this challenge is divided into 3 datasets, referred to as A (Adult speech training set), C1 (Children speech training set) and C2 (Children conversation training set). All dataset combined amount to 400 hours of Mandarin speech data.
AccentDB is a database that contains samples of 4 Indian-English accents, and a compilation of samples from 4 native-English, and a metropolitan Indian-English accent.
5 PAPERS • NO BENCHMARKS YET
Artie Bias Corpus is an open dataset for detecting demographic bias in speech applications.
The i3-video dataset contains "is-it-instructional" annotations for 6.4k videos from Youtube-8M. The videos are considered to be instructional if they focus on real-world human actions accompanied by procedural language that explains what’s happening on screen in reasonable details.
CoVoST is a large-scale multilingual speech-to-text translation corpus. Its latest 2nd version covers translations from 21 languages into English and from English into 15 languages. It has total 2880 hours of speech and is diversified with 78K speakers and 66 accents.
32 PAPERS • NO BENCHMARKS YET
Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.
314 PAPERS • 164 BENCHMARKS
word2word contains easy-to-use word translations for 3,564 language pairs.
Europarl-ST is a multilingual Spoken Language Translation corpus containing paired audio-text samples for SLT from and into 9 European languages, for a total of 72 different translation directions. This corpus has been compiled using the debates held in the European Parliament in the period between 2008 and 2012.
55 PAPERS • NO BENCHMARKS YET
EmoSpeech contains keywords with diverse emotions and background sounds, presented to explore new challenges in audio analysis.
Taskmaster-1 is a dialog dataset consisting of 13,215 task-based dialogs in English, including 5,507 spoken and 7,708 written dialogs created with two distinct procedures. Each conversation falls into one of six domains: ordering pizza, creating auto repair appointments, setting up ride service, ordering movie tickets, ordering coffee drinks and making restaurant reservations.
18 PAPERS • NO BENCHMARKS YET
The Kite database is a multi-modal dataset for the control of unmanned aerial vehicles (UAVs). There are three modalities present in the dataset:
MuST-C currently represents the largest publicly available multilingual corpus (one-to-many) for speech translation. It covers eight language directions, from English to German, Spanish, French, Italian, Dutch, Portuguese, Romanian and Russian. The corpus consists of audio, transcriptions and translations of English TED talks, and it comes with a predefined training, validation and test split.
194 PAPERS • 2 BENCHMARKS
The VOICES corpus is a dataset to promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions.
44 PAPERS • NO BENCHMARKS YET
Speech Commands is an audio dataset of spoken words designed to help train and evaluate keyword spotting systems .
343 PAPERS • 4 BENCHMARKS
AV Digits Database is an audiovisual database which contains normal, whispered and silent speech. 53 participants were recorded from 3 different views (frontal, 45 and profile) pronouncing digits and phrases in three speech modes.
Fongbe Data collected by Fréjus A. A LALEYE
The Oxford-BBC Lip Reading Sentences 2 (LRS2) dataset is one of the largest publicly available datasets for lip reading sentences in-the-wild. The database consists of mainly news and talk shows from BBC programs. Each sentence is up to 100 characters in length. The training, validation and test sets are divided according to broadcast date. It is a challenging set since it contains thousands of speakers without speaker labels and large variation in head pose. The pre-training set contains 96,318 utterances, the training set contains 45,839 utterances, the validation set contains 1,082 utterances and the test set contains 1,242 utterances.
96 PAPERS • 9 BENCHMARKS