README.md

---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: speaker_gender
    dtype: string
  splits:
  - name: train
    num_bytes: 27742339978.527
    num_examples: 1739
  download_size: 32245219567
  dataset_size: 27742339978.527
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
language:
- 'no'
- en
- pl
pretty_name: LnNor
size_categories:
- n<1K
---

# Dataset Card for the LnNor Corpus

<!This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).>

A multilingual dataset of high-quality speech recordings in Norwegian, English, and Polish, designed for research into cross-linguistic influence, multilingual language acquisition, and applications in NLP and speech processing such as ASR, TTS, and linguistic variability modeling. The dataset includes 2,783 recordings, totaling 101 hours, with a size of 50.1 GB. These recordings capture phonological, syntactic, and semantic variability through structured tasks like reading, picture description, and spontaneous conversation.
## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** Magdalena Wrembel, Krzysztof Hwaszcz, Agnieszka Pludra, Anna Skałba, Jarosław Weckwerth, Kamil Malarski, Zuzanna Ewa Cal, Hanna Kędzierska, Tristan Czarnecki-Verner, Anna Balas, Kamil Kaźmierski, Sylwiusz Żychliński, Justyna Gruszecka
- **Funded by:** EEA Financial Mechanism and Norwegian Financial Mechanism
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** Norwegian, English, Polish
- **License:** Creative Commons Attribution 4.0

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://adim.web.amu.edu.pl/en/lnnor-corpus/
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

- **Multilingual ASR training:** Supports building and evaluating ASR systems for multilingual and code-switching scenarios.
- **Linguistic modeling:** Enables research on phonological, syntactic, and semantic variability in multilingual contexts.
- **TTS and speech synthesis:** Provides diverse phonetic data for training multilingual text-to-speech models.
- **Cross-linguistic NLP research:** Facilitates studies on L3 acquisition and cross-linguistic influence in multilinguals.

### Out-of-Scope Use

- **Privacy-violating applications:** The dataset is anonymized and must not be used for speaker identification or biometric analysis tasks.
- **Non-supported languages:** The dataset is tailored for Norwegian, English, and Polish only.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The recordings are systematically labeled using a structured format: **PROJECT_SPEAKER ID_LANGUAGE STATUS_TASK**.

Each component of the label provides specific details:

- **PROJECT:** The project under which the data was collected. Possible values:
  - **A** for ADIM,
  - **C** for CLIMAD.
- **SPEAKER ID:** A unique 8-character identifier assigned to each speaker.
- **LANGUAGE STATUS:** The language used in the recording and its status for the speaker; examples:
  - **L1PL** (Polish as L1),
  - **L2EN** (English as L2),
  - **L3NO** (Norwegian as L3).
- **TASK:** The type of speech task recorded. Examples include:
  - **WR** (word reading),
  - **SR** (sentence reading),
  - **TR** (text reading "The North Wind and the Sun"),
  - **PD** (picture description),
  - **ST** (story telling),
  - **VT** (video story telling),
  - **VD** (video description),
  - **TP/TE** (translation from Polish/English into Norwegian).

If a task type was repeated, sequential numbers (e.g., SR1, SR2) are appended to distinguish iterations.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was developed to advance research in multilingualism and third language (L3) acquisition, with a specific focus on Norwegian, English, and Polish. Its primary aim is to enable studies on cross-linguistic influence, phonological, syntactic and semantic variability, and multilingual language processing. It supports the development of technologies such as multilingual ASR, TTS, and NLP systems.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

The dataset was collected as part of two research projects, CLIMAD (Cross-linguistic Influence in Multilingualism across Domains: Phonology and Syntax) and ADIM (Across-domain Investigations in Multilingualism: Modeling L3 Acquisition in Diverse Settings), which focused on cross-linguistic influence and L3 acquisition in multilingual settings. The dataset comprises recordings from 231 speakers across three languages: Norwegian, English, and Polish. Speakers include L1 Polish learners of Norwegian, L1 English and L1 Norwegian natives, and L2/L3/Ln speakers of English and Norwegian. Speech was elicited using a range of tasks such as word, sentence, and text readings, picture descriptions, video story retelling, and socio-phonetic interviews. Metadata is based on the Language History Questionnaire and includes age, gender, language proficiency, exposure, and other sociolinguistic factors.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

Data were recorded between 2021 and 2024 using Shure SM-35 unidirectional cardioid microphones and Marantz PMD620 recorders, ensuring minimal noise interference. Recordings were captured at 48 kHz, 16-bit resolution [TO BE CONFIRMED]. Some of the recordings were annotated with orthographic and/or phonetic transcriptions and aligned at a word and phoneme level. Metadata includes speaker characteristics, language status (L1, L2, L3/Ln), task type, and audio details. 

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
Source data producers include:
- Polish L1 speakers learning Norwegian as L3/Ln in formal and naturalistic contexts,
- native speakers of Norwegian and English as control groups,
- speakers of English and Norwegian as L2/L3/Ln with diverse L1 backgrounds.


### Annotations 

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

The dataset includes the following types of annotations:

- Orthographic transcriptions (available for selected recordings)
- Phonetic transcriptions (available for selected recordings)
- Word-level alignments (available for selected recordings)
- Phoneme-level alignments (available for selected recordings)
- Speaker metadata (available for all recordings)
  - speaker ID, age, gender, education, current residence, language proficiency (native and additional languages), language status (L1, L2, L3/Ln)
- Audio metadata (available for all recordings)
  - recording ID, task type (e.g., word reading, sentence reading), sampling rate

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

The annotation process combined both automated and manual methods. It consisted of the following steps:

  - Orthographic transcriptions: For Polish and English recordings, transcriptions were generated using a STT tool [NAME NEEDS TO BE ADDED] or created manually by linguists with a high level of proficiency in the respective languages. Norwegian transcriptions were entirely human-generated to ensure high accuracy.
  - Phonetic transcriptions: Phonetic transcriptions were automatically generated using WebMAUS. The output was encoded in SAMPA (Speech Assessment Methods Phonetic Alphabet), ensuring consistency and compatibility with downstream processing.
  - Alignments: Word- and phoneme-level alignments were created using WebMAUS, which produced TextGrids that aligned the transcriptions with corresponding audio files.
  - Speaker metadata: The speaker metadata were collected before the recording sessions through the Linguistic History Questionnaire (LHQ) and supplementary forms provided to participants. These forms were designed to capture detailed linguistic and demographic information, ensuring a comprehensive profile of each speaker.
  - Audio metadata: The audio metadata were automatically captured during the recording process by the equipment used for data collection and embedded into the corresponding audio files.
  - 
#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

The annotations were created under the supervision of a team of linguists and language experts from the Faculty of English at Adam Mickiewicz University in Poznań, Wrocław University of Science and Technology, and the University of Szczecin, all of whom were members of the CLIMAD and ADIM projects. The annotators had extensive experience in transcription, phonetic analysis, and linguistic research in Polish, English, and Norwegian. Their role in the annotation process included:

- providing expertise in phonetic analysis and transcription techniques,
- supervising the use of automated tools such as WebMAUS for phonetic transcriptions and alignments,
- generating transcriptions for recordings that featured languages with limited support in STT tools (i.e., Norwegian) or contained challenging audio (overlapping speech or atypical pronunciations that required careful transcription),
- validating a subset of annotations to ensure high-quality outputs for critical data points.
  
While the majority of annotations were generated using automated tools, the annotators’ oversight ensured consistency and accuracy across the dataset.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

[More Information Needed]

## Dataset Card Authors 

Agnieszka Pludra

Izabela Krysińska

Piotr Kabaciński

## Dataset Card Contact

[email protected]

[email protected]

[email protected]