Dataset Card for [Dataset Name]

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  Data Instances

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  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/DTU54DL/common-accent.

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.

Dataset Card for Common Voice Corpus 16

  Dataset Summary

The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 30328 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 19673 validated hours in 120 languages, but more voices and languages are always added. Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1.

Dataset Card for Common Voice Corpus 6.0

  Dataset Summary

The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 9261 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 7327 validated hours in 60 languages, but more voices and languages are always added. Take a look at the Languages page to request… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_6_0.

Dataset Description

• Homepage: https://projecteaina.cat/tech/]- Point of Contact: langech@bsc.es

Dataset Summary

These are the annotations made by a team of experts on the speakers with more than 1200 seconds recorded in the Catalan set of the Common Voice dataset (v13).

The annotators were initially tasked with evaluating all recordings associated with the same individual. Following that, they were instructed to annotate the speaker's accent, gender, and the overall quality of the recordings.

The accents and genders taken into account are the ones used until version 8 of the Common Voice corpus.

See annotations for more details.

Supported Tasks and Leaderboards

Gender classification, Accent classification.

Languages

The dataset is in Catalan (ca).

Dataset Structure

Instances

Two xlsx documents are published, one for each round of annotations.

The following information is available in each of the documents:

{ 'speaker ID': '1b7fc0c4e437188bdf1b03ed21d45b780b525fd0dc3900b9759d0755e34bc25e31d64e69c5bd547ed0eda67d104fc0d658b8ec78277810830167c53ef8ced24b', 'idx': '31', 'same speaker': {'AN1': 'SI', 'AN2': 'SI', 'AN3': 'SI', 'agreed': 'SI', 'percentage': '100'}, 'gender': {'AN1': 'H', 'AN2': 'H', 'AN3': 'H', 'agreed': 'H', 'percentage': '100'}, 'accent': {'AN1': 'Central', 'AN2': 'Central', 'AN3': 'Central', 'agreed': 'Central', 'percentage': '100'}, 'audio quality': {'AN1': '4.0', 'AN2': '3.0', 'AN3': '3.0', 'agreed': '3.0', 'percentage': '66', 'mean quality': '3.33', 'stdev quality': '0.58'}, 'comments': {'AN1': '', 'AN2': 'pujades i baixades de volum', 'AN3': 'Deu ser d'alguna zona de transició amb el central, perquè no fa una reducció total vocàlica, però hi té molta tendència'}, }

We also publish the document Guia anotació parlants.pdf, with the guidelines the annotators recieved.

Data Fields

speaker ID (string): An id for which client (voice) made the recording in the Common Voice corpus

idx (int): Id in this corpus

AN1 (string): Annotations from Annotator 1

AN2 (string): Annotations from Annotator 2

AN3 (string): Annotations from Annotator 3

agreed (string): Annotation from the majority of the annotators

percentage (int): Percentage of annotators that agree with the agreed annotation

mean quality (float): Mean of the quality annotation

stdev quality (float): Standard deviation of the mean quality

Data Splits

The corpus remains undivided into splits, as its purpose does not involve training models.

Dataset Creation

Curation Rationale

During 2022, a campaign was launched to promote the Common Voice corpus within the Catalan-speaking community, achieving remarkable success. However, not all participants provided their demographic details such as age, gender, and accent. Additionally, some individuals faced difficulty in self-defining their accent using the standard classifications established by specialists.

In order to obtain a balanced corpus with reliable information, we have seen the the necessity of enlisting a group of experts from the University of Barcelona to provide accurate annotations.

We release the complete annotations because transparency is fundamental to our project. Furthermore, we believe they hold philological value for studying dialectal and gender variants.

Source Data

The original data comes from the Catalan sentences of the Common Voice corpus.

Initial Data Collection and Normalization

We have selected speakers who have recorded more than 1200 seconds of speech in the Catalan set of the version 13 of the Common Voice corpus.

Who are the source language producers?

The original data comes from the Catalan sentences of the Common Voice corpus.

Annotations

Annotation process

Starting with version 13 of the Common Voice corpus we identified the speakers (273) who have recorded more than 1200 seconds of speech.

A team of three annotators was tasked with annotating:

if all the recordings correspond to the same person

the gender of the speaker

the accent of the speaker

the quality of the recording

They conducted an initial round of annotation, discussed their varying opinions, and subsequently conducted a second round.

We release the complete annotations because transparency is fundamental to our project. Furthermore, we believe they hold philological value for studying dialectal and gender variants.

Who are the annotators?

The annotation was entrusted to the CLiC (Centre de Llenguatge i Computació) team from the University of Barcelona. They selected a group of three annotators (two men and one woman), who received a scholarship to do this work.

The annotation team was composed of:

Annotator 1: 1 female annotator, aged 18-25, L1 Catalan, student in the Modern Languages and Literatures degree, with a focus on Catalan.

Annotators 2 & 3: 2 male annotators, aged 18-25, L1 Catalan, students in the Catalan Philology degree.

1 female supervisor, aged 40-50, L1 Catalan, graduate in Physics and in Linguistics, Ph.D. in Signal Theory and Communications.

To do the annotation they used a Google Drive spreadsheet

Personal and Sensitive Information

The Common Voice dataset consists of people who have donated their voice online. We don't share here their voices, but their gender and accent. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

Considerations for Using the Data

Social Impact of Dataset

The ID come from the Common Voice dataset, that consists of people who have donated their voice online.

You agree to not attempt to determine the identity of speakers in the Common Voice dataset.

The information from this corpus will allow us to train and evaluate well balanced Catalan ASR models. Furthermore, we believe they hold philological value for studying dialectal and gender variants.

Discussion of Biases

Most of the voices of the common voice in Catalan correspond to men with a central accent between 40 and 60 years old. The aim of this dataset is to provide information that allows to minimize the biases that this could cause.

For the gender annotation, we have only considered "H" (male) and "D" (female).

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)

This work has been promoted and financed by the Generalitat de Catalunya through the Aina project.

Licensing Information

This dataset is licensed under a CC BY 4.0 license.

It can be used for any purpose, whether academic or commercial, under the terms of the license. Give appropriate credit, provide a link to the license, and indicate if changes were made.

Citation Information

DOI

Contributions

The annotation was entrusted to the STeL team from the University of Barcelona.

ZZZtong/common-accent-all-features dataset hosted on Hugging Face and contributed by the HF Datasets community

Around 34 percent of Japanese residents in Fukuoka claimed that the prefecture can take pride in the charming dialect of its locals, according to a survey conducted in November 2021 among locals of the 47 prefectures in Japan. The dialect of Fukuoka prefecture is part of the Kyushu dialects, which also includes the dialects of the second-ranked prefecture Nagasaki.

Customs records of are available for ACCENT DECOR COMMON GROUND CORKCICLE. Learn about its Importer, supply capabilities and the countries to which it supplies goods

ZZZtong/common-accent-YAMnet dataset hosted on Hugging Face and contributed by the HF Datasets community

Customs records of are available for ONE HUNDRED COMMON GROUND BLOCK DESIGN ACCENT DECOR TRUE BRANDS JR WILLIAM TWO S. COMPANY LAZAR DECO. Learn about its Importer, supply capabilities and the countries to which it supplies goods

ZZZtong/common-accent-MelSpec-MFCC dataset hosted on Hugging Face and contributed by the HF Datasets community

The BRWDS (Bangla Regional Word Dataset) is a comprehensive collection of commonly used Bengali words that highlights the linguistic diversity across 8 distinct divisions in Bangladesh. This dataset aims to tackle the challenges posed by regional accents and variations in Bengali, which can create barriers to communication. The dataset covers words from the following divisions: Dhaka, Chittagong, Mymensingh, Sylhet, Rajshahi, Khulna, Barishal, and Rangpur. In total, it includes 347 Bengali words that are frequently used in daily conversations across these regions. While Bengali is spoken across all these divisions, each region has its own unique accent, leading to variations in pronunciation and word usage, which are captured in this dataset.

To create this dataset, 12 native speakers from the 8 divisions, as well as one additional district, contributed by providing word samples. The data is stored in XLSX format, making it easily accessible for further research. This dataset has several potential applications, including the development of systems that can automatically detect regional variations in Bengali text, enabling better localization and understanding of regional dialects. It can also help minimize communication barriers caused by accent differences within Bangladesh by offering a more standardized understanding of regional variations. Additionally, the dataset can be used to translate regional words into standard Bengali (Chaste Bengali), making it easier for people to understand each other. The dataset also supports research into linguistic diversity and provides a foundation for future advancements in speech and text processing technologies. The dataset has been reviewed and evaluated by 9 authentic speakers from each division to ensure its accuracy and proper representation of the regional language variations.

Looking forward, the dataset can be further enriched by adding voice data, which would support more advanced research in areas such as speech recognition, accent detection, and machine translation for regional language variants.

Data was situated in Bangla RDS.xlsv . In sheet 1 named Region wise data was collected and evaluated on other sheet named categorize data where all the data was categorized and organize according to common chaste words.

ZZZtong/common-accent-vgg-ready dataset hosted on Hugging Face and contributed by the HF Datasets community

The West Yorkshire Regional English Database (WYRED) consists of approximately 200 hours of high-quality audio recordings of 180 West Yorkshire (British English) speakers. All participants are male between the ages of 18-30, and are divided evenly (60 per region) across three boroughs within West Yorkshire (Northern England): Bradford, Kirklees, and Wakefield. Speakers participated in four spontaneous speaking tasks. The first two tasks relate to a mock crime where the participant speaks to a police officer (Research Assistant 1) followed by an accomplice (Research Assistant 2). Speakers returned a minimum of 6 days later at which point they were paired with someone from their borough and recorded having a conversation on any topics they wish. The final task is an experimental task in which speakers are asked to leave a voicemail message related to the fictitious crime from the first recording session. In total, each speaker participated in approximately 1 hour of spontaneous speech recordings. The primary motivation for the construction of the West Yorkshire Regional English Database (WYRED) was to provide a collection of regionally stratified speech recordings (by boroughs) from within a single, politically defined region (a county). The corpus aims to facilitate research on methodological issues surrounding the delimitation of the reference population when considering the typicality of a speech sample for a given forensic speaker comparison case, while also providing valuable insight into the West Yorkshire accent(s).

Forensic speech science (FSS) - an applied sub-discipline of phonetics - has come to play a critical role in criminal cases involving voice evidence. Within FSS, Forensic speaker comparison (FSC) involves the comparison of a criminal recording (e.g. a threatening phone call), and a known suspect sample (e.g. a police interview). It is the role of an expert forensic phonetician to advise the trier of fact (e.g. judge or jury) on the likelihood of the two samples coming from the same speaker. There are two important elements involved in making such a comparison. First, the expert will carry out an assessment of the similarity of the speech characteristics in the criminal recording and the suspect sample. Second, the expert will assess the degree to which the same speech features for the criminal sample can be considered to be typical for a given speaker group. The speaker group will typically be defined by age, sex and geographical region (or accent). This second element is critical in providing context for the first; the suspect could have speech very similar to that in the criminal recording but this could be purely coincidental if they exhibit speech characteristics that are common to their speaker group. In contrast, if the criminal and suspect are observed as having speech features considered as being atypical for their speaker group then this would provide strong evidence for it being the same speaker.

One complication associated with FSC is that data to estimate whether a speech feature is typical or atypical for the given speaker group, commonly known as population data, are scarcely available. Population data are typically obtained by collecting a set of recordings containing the voices of a homogeneous group of speakers similar in age, sex, and geographical region (or accent). Unfortunately, the time and expense involved in the collection of population data means that forensic phoneticians face a huge challenge in obtaining such data for casework. This problem is further complicated by the high degree of variation that exists in speech across different speaker groups. Methodological research in the field of FSS has demonstrated that identifying the correct population for a FSC is vital in accurately representing the strength of evidence. It is largely for these reasons that experts argue that the biggest problem facing the field is the limited availability of population data.

The primary aim of this research is to explore a novel set of proposed methods that seek to remedy the aforementioned problems. The current lack of a platform on which to exchange data means that population data for a specific speaker group might have already been collected, unbeknown to experts in need of such data. This project intends to bring an end to this type of scenario by developing an international platform on which to share data, and also encouraging fellow researchers and experts to participate in data sharing. In addition, the project will explore the extent to which population data are generalizable; specifically, this will entail identifying the geographical (or regional accent) level at which speaker groups can be defined. For example, an expert might define a population group as having a Wakefield accent, when in actuality a population defined more generally as West Yorkshire would suffice. This would clearly have implications for the way in which population data would be collected.

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Dataset Card for Common Voice Corpus 9.0

  Dataset Summary

The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 20217 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 14973 validated hours in 93 languages, but more voices and languages are always added. Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0.

ZZZtong/common-accent-processed-raw dataset hosted on Hugging Face and contributed by the HF Datasets community

The most spoken dialect in Italy is South Italian. This macro-group includes varieties spoken in the regions of Campania, Calabria, Basilicata, Abruzzo, Apulia, and Molise as well as in some areas of Lazio, Marche, and Umbria, three regions of the Center. As of 2018, South Italian counted 7.5 million speakers. The second most spoken dialect, Sicilian, had about five million speakers.

Enhancement of comprehension and more profound understanding of accents were the most common reasons why American adults use subtitles while watching TV in a known language, according to a survey conducted between June and July 2023. Another 33 percent of the respondents stated that they did so because they were in a noisy environment.

The data was performed by employing standard Twitter API on Arabic tweets and code-mixing datasets. The Data was carried out for a duration of three months, specifically from April 2023 to June 2023. This was done via a combination of keyword, thread-based searches, and profile-based search approaches as. A total of 120 terms, including various versions, which were used to identify tweets containing code-mixing concerning regional hate speech. To conduct a thread-based search, we have incorporated hashtags that are related to contentious subjects that are deemed essential markers for hateful speech. Throughout the data-gathering phase, we kept an eye on Twitter trends and designated ten hashtags for information retrieval. Given that hateful tweets are usually less common than regular tweets, we expanded our dataset and improved the representation of the hate class by incorporating the most impactful terms from a lexicon of religious hate terms (Albadi et al., 2018). We gathered exclusively original Arabic tweets for all queries, excluding retweets and non-Arabic tweets. In all, we obtained 200,000 Twitter data, of which we sampled 35k tweets for annotation.

Dataset Card for Common Voice Corpus 11.0

  Dataset Summary

The Common Voice dataset consists of a unique MP3 and corresponding text file. Many of the 24210 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can help improve the accuracy of speech recognition engines. The dataset currently consists of 16413 validated hours in 100 languages, but more voices and languages are always added. Take a look at the Languages page to… See the full description on the dataset page: https://huggingface.co/datasets/taqwa92/cm.trial.