warisqr007/GAPS-nptel

GAPS: Golden-Aligned Parallel Speech Corpus

Overview

GAPS (Golden-Aligned Parallel Speech) is a multi-corpus dataset designed for foreign accent conversion.

The dataset provides parallel speech triplets consisting of:

• Original non-native speech
• Parallel native speech
• Golden speaker speech — synthetic speech that preserves the non-native speaker’s timbre and timing (including pauses) while exhibiting native pronunciation

along with the corresponding text transcript.

GAPS is constructed to support both offline accent conversion and streaming, low-latency pronunciation correction, and is used in our work on streaming foreign accent conversion for voice anonymization.

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Dataset Structure

This repository extends the GAPS(https://huggingface.co/datasets/warisqr007/GAPS) to the NPTEL lecture corpus

The dataset contains the following main columns:

Column name	Type	Description
original	Audio	Original non-native speech
parallel_native	Audio	Parallel native speech
golden_speaker	Audio	Golden speaker speech (synthetic)
transcript	string	Text transcription

All audio is single-channel, 16 kHz.

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Dataset Statistics

Speakers	Duration (approx.)
TBD	TBD hours

(Statistics will be updated soon.)

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Data Construction

Source Corpora

GAPS-nptel extends GAPS to include NPTEL (BhasaAnuvaad) that contains lecture speech from Indian English speakers. The original datasets are not redistributed in raw form.
GAPS provides processed, aligned, and synthesized derivatives, following the original licenses.

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Golden Speaker Generation

Golden speaker utterances are generated entirely offline using a two-stage, reference-free accent conversion pipeline, redesigned for duration preservation and streaming compatibility.

For each non-native / native utterance pair:

1. Content Extraction
Linguistic content representations are extracted independently from the native and non-native utterances using a speaker-independent content encoder.

2. Silence-Aware DTW Alignment

• Voice Activity Detection (VAD) is applied to remove silence regions.
• Dynamic Time Warping (DTW) is performed in the content embedding space.
• Native content embeddings are temporally aligned to the non-native utterance.
• Silence segments are re-inserted to preserve the original non-native timing and rhythm.

3. Golden Speaker Synthesis

• Aligned native content embeddings provide native pronunciation.
• Non-native speaker embeddings provide speaker identity (timbre).
• Duration and rhythm follow the non-native utterance.
• Waveforms are synthesized using a zero-shot voice conversion system and neural vocoder.

The resulting golden speaker speech differs from the original non-native speech only in accent, making it suitable as supervision for pronunciation correction and accent translation.

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Intended Use

GAPS is intended for research on:

• Foreign accent conversion (FAC)
• Accent-aware speaker anonymization
• Streaming pronunciation correction
• Accent analysis and evaluation

The dataset is not intended for commercial use, unless explicitly permitted under the original licenses.

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Example Usage

from datasets import load_dataset

ds = load_dataset("warisqr007/GAPS-nptel")

# Access a specific split
sample = ds[0]

# Audio is loaded lazily
audio = sample["original"]
print(audio["sampling_rate"], audio["array"].shape)

print(sample["transcript"])

Licenses and Usage Terms

Each subset of GAPS follows the same license as its original dataset.

NPTEL / BhasaAnuvaad

• License: CC BY-NC 4.0
• Summary: https://creativecommons.org/licenses/by-nc/4.0/
• Full license: https://creativecommons.org/licenses/by-nc/4.0/legalcode
• Hugging Face dataset: https://huggingface.co/datasets/ai4bharat/NPTEL

This processed dataset follows the same license. For any usage not covered by this license, please contact the dataset authors and cite the BhasaAnuvaad paper.

Citation

If you use GAPS in your research, please cite:

GAPS (this dataset)

@article{gaps2026,
  title   = {GAPS: Golden-Aligned Parallel Speech Corpus for Accent Conversion and Anonymization},
  author  = {TBD},
  journal = {TBD},
  year    = {2026}
}

(Placeholder — update once the paper is public.)

NPTEL / BhasaAnuvaad

@article{jain2024bhasaanuvaad,
  title   = {BhasaAnuvaad: A Speech Translation Dataset for 14 Indian Languages},
  author  = {Jain, Sparsh and Sankar, Ashwin and Choudhary, Devilal and Suman, Dhairya and Narasimhan, Nikhil and Khan, Mohammed Safi Ur Rahman and Kunchukuttan, Anoop and Khapra, Mitesh M and Dabre, Raj},
  journal = {arXiv preprint arXiv:2411.04699},
  year    = {2024}
}