The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.
505 PAPERS • 12 BENCHMARKS
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.
321 PAPERS • 164 BENCHMARKS
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
BEAT has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with \textit{facial expressions}, \textit{emotions}, and \textit{semantics}, in addition to the known correlation with \textit{audio}, \textit{text}, and \textit{speaker identity}. Based on this observation, we propose a baseline model, \textbf{Ca}scaded \textbf{M}otion \textbf{N}etwork \textbf{(CaMN)}, which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (\textbf{SRGR}). Qualitative and quantitative experiments demonstrate metrics' validness, ground truth data quality, and baseline's state-of-the-art performance. To the best of our knowledge,
38 PAPERS • 1 BENCHMARK
AVSpeech is a large-scale audio-visual dataset comprising speech clips with no interfering background signals. The segments are of varying length, between 3 and 10 seconds long, and in each clip the only visible face in the video and audible sound in the soundtrack belong to a single speaking person. In total, the dataset contains roughly 4700 hours of video segments with approximately 150,000 distinct speakers, spanning a wide variety of people, languages and face poses.
37 PAPERS • NO BENCHMARKS YET
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
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.
33 PAPERS • NO BENCHMARKS YET
CVSS is a massively multilingual-to-English speech to speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice speech corpus and the CoVoST 2 speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-of-the-art TTS systems
20 PAPERS • 1 BENCHMARK
MIR-1K (Multimedia Information Retrieval lab, 1000 song clips) is a dataset designed for singing voice separation. It contains:
20 PAPERS • NO BENCHMARKS YET
The iKala dataset is a singing voice separation dataset that comprises of 252 30-second excerpts sampled from 206 iKala songs (plus 100 hidden excerpts reserved for MIREX data mining contest). The music accompaniment and the singing voice are recorded at the left and right channels respectively. Additionally, the human-labeled pitch contours and timestamped lyrics are provided.
CH-SIMS is a Chinese single- and multimodal sentiment analysis dataset which contains 2,281 refined video segments in the wild with both multimodal and independent unimodal annotations. It allows researchers to study the interaction between modalities or use independent unimodal annotations for unimodal sentiment analysis.
16 PAPERS • 1 BENCHMARK
We construct a dataset named CPED from 40 Chinese TV shows. CPED consists of multisource knowledge related to empathy and personal characteristic. This knowledge covers 13 emotions, gender, Big Five personality traits, 19 dialogue acts and other knowledge.
15 PAPERS • 3 BENCHMARKS
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic co-speech dataset. BEAT2 combines MoShed SMPLX body with FLAME head parameters and further refines the modeling of head, neck, and finger movements, offering a community-standardized, high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer, facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover, EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enh
8 PAPERS • 2 BENCHMARKS
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese code-switching corpus collected in Hong Kong. ASCEND includes 23 bilinguals that are fluent in both Chinese and English and consists of 10.62 hours clean speech corpus.
6 PAPERS • NO BENCHMARKS YET
The MISP2021 challenge dataset is a collection of audio-visual conversational data recorded in a home TV scenario using distant multi-microphones. The dataset captures interactions between several individuals who are engaged in conversations in Chinese while watching TV and interacting with a smart speaker/TV in a living room. The dataset is extensive, comprising 141 hours of audio and video data, which were collected using far/middle/near microphones and far/middle cameras in 34 real-home TV rooms. Notably, this corpus is the first of its kind to offer a distant multimicrophone conversational Chinese audio-visual dataset. Furthermore, it is also the first large vocabulary continuous Chinese lip-reading dataset specifically designed for the adverse home-TV scenario.
3 PAPERS • NO BENCHMARKS YET
The ODSQA dataset is a spoken dataset for question answering in Chinese. It contains more than three thousand questions from 20 different speakers.
A Rich Annotated Mandarin Conversational (RAMC) Speech Dataset, including 180 hours of Mandarin Chinese dialogue, 150, 10 and 20 hours for the training set, development set and test set respectively. It contains 351 multi-turn dialogues, each of which is a coherent and compact conversation centered around one theme.
1 PAPER • NO BENCHMARKS YET
CHORD is the first chorus recognition dataset containing 627 songs for public use.