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22 dataset results for Mandarin Chinese

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

AISHELL-1

AISHELL-1 is a corpus for speech recognition research and building speech recognition systems for Mandarin.

163 PAPERS • 1 BENCHMARK

ACE 2005 (ACE 2005 Multilingual Training Corpus)

ACE 2005 Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2005 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities, relations and events by the Linguistic Data Consortium (LDC) with support from the ACE Program and additional assistance from LDC.

62 PAPERS • 9 BENCHMARKS

AISHELL-2

AISHELL-2 contains 1000 hours of clean read-speech data from iOS is free for academic usage.

49 PAPERS • 4 BENCHMARKS

ACE 2004

ACE 2004 (ACE 2004 Multilingual Training Corpus)

ACE 2004 Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2004 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities and relations and was created by Linguistic Data Consortium with support from the ACE Program, with additional assistance from the DARPA TIDES (Translingual Information Detection, Extraction and Summarization) Program. The objective of the ACE program is to develop automatic content extraction technology to support automatic processing of human language in text form. In September 2004, sites were evaluated on system performance in six areas: Entity Detection and Recognition (EDR), Entity Mention Detection (EMD), EDR Co-reference, Relation Detection and Recognition (RDR), Relation Mention Detection (RMD), and RDR given reference entities. All tasks were evaluated in three languages: English, Chinese and Arabic.

46 PAPERS • 5 BENCHMARKS

WenetSpeech

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.

38 PAPERS • 1 BENCHMARK

AISHELL-3

AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus which could be used to train multi-speaker Text-to-Speech (TTS) systems. The corpus contains roughly 85 hours of emotion-neutral recordings spoken by 218 native Chinese mandarin speakers and total 88035 utterances. Their auxiliary attributes such as gender, age group and native accents are explicitly marked and provided in the corpus. Accordingly, transcripts in Chinese character-level and pinyin-level are provided along with the recordings. The word & tone transcription accuracy rate is above 98%, through professional speech annotation and strict quality inspection for tone and prosody.

31 PAPERS • NO BENCHMARKS YET

Belebele

Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.

17 PAPERS • NO BENCHMARKS YET

MagicData-RAMC

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

FMFCC-A

FMFCC-A is a large publicly-available Mandarin dataset for synthetic speech detection, which contains 40,000 synthesized Mandarin utterances that generated by 11 Mandarin TTS systems and two Mandarin VC systems, and 10,000 genuine Mandarin utterance collected from 58 speakers. The FMFCCA dataset is divided into the training, development and evaluation sets, which are used for the research of detection of synthesised Mandarin speech under various previously unknown speech synthesis systems or audio post-processing operations.

5 PAPERS • NO BENCHMARKS YET

BiPaR

BiPaR is a manually annotated bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support monolingual, multilingual and cross-lingual reading comprehension on novels. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written in parallel in two languages. BiPaR is diverse in prefixes of questions, answer types and relationships between questions and passages. Answering the questions requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality.

4 PAPERS • NO BENCHMARKS YET

CSRC

CSRC (Children Speech Recognition Challenge)

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.

4 PAPERS • NO BENCHMARKS YET

DISRPT2019

DISRPT2019 (DISRPT2019 shared task on Discourse Unit Segmentation and Connective Detection)

The DISRPT 2019 workshop introduces the first iteration of a cross-formalism shared task on discourse unit segmentation. Since all major discourse parsing frameworks imply a segmentation of texts into segments, learning segmentations for and from diverse resources is a promising area for converging methods and insights. We provide training, development and test datasets from all available languages and treebanks in the RST, SDRT and PDTB formalisms, using a uniform format. Because different corpora, languages and frameworks use different guidelines for segmentation, the shared task is meant to promote design of flexible methods for dealing with various guidelines, and help to push forward the discussion of standards for discourse units. For datasets which have treebanks, we will evaluate in two different scenarios: with and without gold syntax, or otherwise using provided automatic parses for comparison.

4 PAPERS • NO BENCHMARKS YET

EMOVIE

EMOVIE is a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation.

3 PAPERS • NO BENCHMARKS YET

AM2iCo (Adversarial and Multilingual Meaning in Context)

AM2iCo is a wide-coverage and carefully designed cross-lingual and multilingual evaluation set. It aims to assess the ability of state-of-the-art representation models to reason over cross-lingual lexical-level concept alignment in context for 14 language pairs.

2 PAPERS • NO BENCHMARKS YET

K-SportsSum

K-SportsSum is a sports game summarization dataset with two characteristics: (1) K-SportsSum collects a large amount of data from massive games. It has 7,854 commentary-news pairs. To improve the quality, K-SportsSum employs a manual cleaning process; (2) Different from existing datasets, to narrow the knowledge gap, K-SportsSum further provides a large-scale knowledge corpus that contains the information of 523 sports teams and 14,724 sports players.

2 PAPERS • NO BENCHMARKS YET

CCSE (Chinese Character Stroke Extraction)

Chinese Character Stroke Extraction (CCSE) is a benchmark containing two large-scale datasets: Kaiti CCSE (CCSE-Kai) and Handwritten CCSE (CCSE-HW). It is designed for stroke extraction problems.

1 PAPER • NO BENCHMARKS YET

OTEANNv3

This dataset contains orthographic samples of words in 19 languages (ar, br, de, en, eno, ent, eo, es, fi, fr, fro, it, ko, nl, pt, ru, sh, tr, zh). Each sample contains two text features: a Word (the textual representation of the word according to its orthography) and a Pronunciation (the highest-surface IPA pronunciation of the word as pronunced in its language).

1 PAPER • NO BENCHMARKS YET

UNER v1

UNER v1 (Universal NER v1)

UNER v1 adds an NER annotation layer to 18 datasets (primarily treebanks from UD) and covers 12 geneologically and ty- pologically diverse languages: Cebuano, Danish, German, English, Croatian, Portuguese, Russian, Slovak, Serbian, Swedish, Tagalog, and Chinese4. Overall, UNER v1 contains nine full datasets with training, development, and test splits over eight languages, three evaluation sets for lower-resource languages (TL and CEB), and a parallel evaluation benchmark spanning six languages.

1 PAPER • 31 BENCHMARKS

mDRT

mDRT (Multilingual Diagnostic Rhyme Test)

We present a multilingual test set for conducting speech intelligibility tests in the form of diagnostic rhyme tests. The materials currently contain audio recordings in 5 languages and further extensions are in progress. For Mandarin Chinese, we provide recordings for a consonant contrast test as well as a tonal contrast test. Further information on the audio data, test procedure and software to set up a full survey which can be deployed on crowdsourcing platforms is provided in our paper [arXiv preprint] and GitHub repository. We welcome contributions to this open-source project.

1 PAPER • NO BENCHMARKS YET

xMIND

xMIND (A Multilingual Dataset for Cross-lingual News Recommendation)

xMIND is an open, large-scale multilingual news dataset for multi- and cross-lingual news recommendation. xMIND is derived from the English MIND dataset using open-source neural machine translation (i.e., NLLB 3.3B).

1 PAPER • NO BENCHMARKS YET

MCCSD

MCCSD (Mandarin Chinese Cued Speech Dataset)

This MCCS dataset is the first large-scale Mandarin Chinese Cued Speech dataset. This dataset covers 23 major categories of scenarios (e.g, communication, transportation and shoping) and 72 subcategories of scenarios (e.g, meeting, dating and introduction). It is recorded by four skilled native Mandarn Chinese Cued Speech cuers with portable cameras on the mobile phones. The Cued Speech videos are recorded with 30fps and 1280x720 format. We provide the raw Cued Speech videos, text file (with 1000 sentences) and corresponding annotations which contains two kind of data annotation. One is continuious video annotation with ELAN, the other is discrete audio annotations with Praat.

0 PAPER • NO BENCHMARKS YET

Datasets

22 dataset results for Mandarin Chinese