The Meitei Speech Dataset, developed as part of the Speech Datasets and Models for Tibeto-Burman Languages (Project SpeeD-TB) funded under Mission Bhashini, is a transcribed speech corpus of the language. The full dataset comprises over 200 hours of high-quality audio recordings paired with accurate transcriptions in both IPA and Roman script, making it ** one of the largest speech resources for the language**, which not only enables building and evaluating voice models in low-resource linguistic settings but also enables large-scale linguistic description and documentation of the language. The audio data captures a diverse range of speakers across different age groups, genders, and education levels, ensuring variability in pronunciation, speech patterns, and tone. It includes both spontaneous and read speech collected in naturalistic and semi-controlled environments, thereby reflecting real-world linguistic usage. The transcriptions are carefully prepared and normalised to maintain consistency, supporting robust model training. This dataset also contributes to the preservation and digital documentation of the language and culture by transforming oral knowledge into structured, machine-readable formats.

Almost 60% of the data in the corpus is included from domains of agriculture, education and science & technology. The rest of the data is from varied domains including culture, lifecycle, sports, entertainment, healthcare and oral history, thereby giving extensive coverage. We have also used a variety of elicitation methods for collecting the data, including translations, narrations, lectures, role-play, spontaneous conversations, interviews and picture and video descriptions. The released dataset is meticulously mapped to rich metadata, including demographic and linguistic metadata of the speakers, domains, elicitation methods and individual prompts. The audio included in the current dataset is already sliced at sentence level, thereby ready to be integrated into the model training pipeline out-of-the-box.

The overall dataset of the project is collected over multiple phases and using multiple questionnaires. All the questionnaires and datasets are being released as part of the project.

About

Speed-Tb Phase 1 Meitei Narration

About Meitei

Meitei (also known as Manipuri and Meetei; ISO 639-3: mni, Glottocode: meit1246 / mani1292) belongs to the Tibeto-Burman language family. Its precise subgrouping within Tibeto-Burman remains a subject of academic debate, often classified within its own independent branch or grouped tentatively with the Kuki-Chin-Naga languages. According to the 2011 Census of India, there are approximately 1.76 million native speakers of Manipuri in India. The language is primarily spoken in the northeastern state of Manipur, where it serves as the official state language and the lingua franca among diverse ethnic groups. Significant speaker communities also exist in the neighbouring states of Assam, Tripura, and Nagaland. Owing to its official status as one of the scheduled languages of India, a large amount of resource development work has been undertaken for the language. Our current corpus adds to the ever-increasing corpora being collected for the language.

Ethical Considerations, IPR and Attribution

This repository represents our commitment to not only fair remuneration to the speakers of the language but also to co-ownership and equal IPR to all the contributors who have built the dataset. We believe this is the first step to move away from extractive data collection and use practices and ensure fairness in our treatment of the community members. As such, we have listed all speakers and transcribers as Contributors to the dataset. This dataset is only licensed to other researchers for use in their research projects. More details about licensing and commercial use conditions are given in the License and Commercial Use sections.

Tools

We employed Karya and Atekho for collecting and recording data. The complete dataset is transcribed and exported using MATra Lab. Both Atekho and MAtra Lab are part of the LiFE Suite Ecosystem, developed by Unreal Tece LLP.

Speakers

Abul F, Asem Pari Tomthin, Babloo, Barnali O, Bidyanath, Bijen M, Bikramjeet, Budhi, Ch Rajendrakumar, DP Rabia Zia, Dhanabir Thounaojam, Dhanapati, Dhanapriya, Gatlin haobam, Gibson, Gurumayum Reeti Devi , James, Jayshree, Jila, KSHETRIMAYUM SURIYA , Khullakpam Seemarani , Kongbam Priya Chanu , Konsam Rameshwori Devi, LAIPHRAKPAM RIDWAN CHANU, LAISHRAM Niranjana Devi, LOUREMBAM NONGTHOUNGANBA, Lairenjam Lanchenbi Chanu, Laishram Ashalata Devi, Laishram Nabachandra Singh, Laishram Niranjana Devi, Lakshmipriya Shrimayum, Laxmi L, Leishangthem Dhanapriyari Devi , Leishungbam Kitri , Liza, Lourembam Nongthounganba , Lucy Laishram, Lucy Laishram , MT, Maibam B Singh, Mangansana, Meghachandra Salam, NAMEIRAKPAM AMIT , Nameirakpam Amit, Neha Sinam, Nibha Khuman, Nilakanta, Ningombam Marina Chanu, Ningthoujam Franand Devi, Nongmaithem Preety, Nutunbala Lourembam, Pebam Shayarani Devi , Ramesh, Ratna Thounajam, Roshibala Yumnam, S Ningthoujam , Sagolsem Urmila, Siltu Laishram, Subhaschandra T, Sujata, Surjit, Tampakmayum Rosy , Thokchom Priyangka Devi, Thoudam Singh, Thoudam Vilip Singh, YUMNAM ABOY, Yumkham Rachita Roy , neha sinam

Annotators

Niran, SpeeD-TB Project, amitnameirakpam, meiraba

Structure

The dataset is organized by splits (e.g. train, test, validation).

Each row contains audio, audio-level metadata, prompt metadata and speaker metadata as described below:

Audio and Audio-level Metadata

audio: The audio file path (loaded as Audio feature in HF Datasets)
audio_id: Unique identifier for the audio
filename: Original filename
sentence-<SCRIPT>-transcription: Text transcription of the audio in the given script
speaker_id: Identifier for the speaker
boundaryID: Identifier for the boundary
start_time: Start time of the segment in seconds
end_time: End time of the segment in seconds

Prompt Metadata

'Q_Id`: Unique identifier for the question or prompt associated with the audio (maps to the question in the LiFE Questionnaire projects and accessible through the questionnaire repo)
'Domain': Domain of the audio (e.g., Agriculture, Education, General, etc.)
'Elicitation_Method`: Method used to elicit the speech (e.g., Translation, Narration, etc.)
Target: An optional field for translation indicating the grammatical structure being targeted for elicitation using the sentence.

Speaker Metadata

ageGroup: Age group of the speaker (e.g., 18-30, 30-50, etc.)
gender: Gender of the speaker
educationLevel: Education level of the speaker
educationMediumUpto12-list: Medium of education up to 12th grade (list of comma-separated values)
'educationMediumAfter12-list`: Medium of education after 12th grade (list of comma-separated values)
otherLanguages-list: Languages spoken by the speaker (list of comma-separated values) - this usually excludes the primary language of the dataset and is used to capture multilingualism in speakers.
nativeLanguage: The native language of the speaker (optional field if data is collected from non-native speakers of the language)
placeOfRecording: The location where the audio was recorded (optional field) or the native place of the speaker (if known)
typeOfplace: Whether the placeOfRecording mentioned is City, Town or Village.

Additional Metadata

textgrid_json: TextGrid data converted to JSON format In addition to any other metadata fields provided during upload are optionally included.

License

This work is licensed under a CC-By-NC-SA-4.0 license. This license allows reusers to distribute, remix, adapt, build upon, and incorporate into software systems, the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, build upon, or incorporate into software systems, you must license the modified material, including material generated by the software system, under identical terms, and license the software system under the GNU General Public License.

Commercial Use

If you are interested in using this dataset for commercial purposes, please contact us (contact [at] unreal-tece[dot]co[dot]in). Profits from commercial licensing will be distributed as royalties to the community members who contributed to this dataset.

Contact

For questions, issues, or contributions, open an issue on the dataset repository or contact us directly (contact [at] unreal-tece[dot]co[dot]in).

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