RIR-Mega-Speech

Dataset Summary

RIR-Mega-Speech is a large-scale reverberant speech corpus created by convolving LibriSpeech utterances with simulated room impulse responses (RIRs sampled from the RIR-Mega collection). Each reverberant utterance includes per-file acoustic metadata computed from the source RIR, enabling controlled analysis of reverberation effects on speech processing systems.

This dataset emphasizes transparency and reproducibility: acoustic metrics are explicitly defined, and the underlying RIR corpus, generation scripts, and evaluation methodology are publicly available.

Relationship to RIR-Mega

This dataset is derived from the RIR-Mega room impulse response corpus:

• Hugging Face: https://huggingface.co/datasets/mandipgoswami/rirmega
• Zenodo DOI: https://doi.org/10.5281/zenodo.17387402
• arXiv paper: https://arxiv.org/abs/2510.18917
• rirmega-speech arXiv paper: https://arxiv.org/abs/2601.19949v1

If you require access to the raw RIR waveforms or wish to generate alternative speech corpora, please refer to the RIR-Mega dataset directly.

Motivation

Despite extensive work on reverberant speech, comparing methods remains difficult because many corpora lack per-file acoustic annotations or provide limited documentation for reproduction. RIR-Mega-Speech is intended as a standardized resource where acoustic conditions are explicit and results can be independently verified.

Source Data

• Clean speech: LibriSpeech (public-domain audiobook speech)
• Room impulse responses: RIR-Mega (simulated, physics-based)

Data Generation

For each clean utterance (x[n]), a room impulse response (h[n]) sampled from RIR-Mega is applied via time-domain convolution: [ y[n] = (x * h)[n]. ] RIRs that produce clipping or fail metadata validation are excluded.

Acoustic Metadata

Metadata is computed from the source RIR prior to convolution:

• RT60: Schroeder backward integration with linear fit between −5 dB and −35 dB, extrapolated to −60 dB (ISO 3382-1 compliant)
• DRR: Direct-to-reverberant ratio using a 2.5 ms direct-only window centered on the first arrival
• C50: Clarity index with a 50 ms boundary
• LUFS: Integrated loudness (ITU-R BS.1770)
• duration_s: Utterance duration in seconds

Note: The DRR definition uses a narrow direct-only window and may produce very low values for weak direct arrivals.

Dataset Structure

rir-mega-speech/ ├── audio/ │ ├── train/ │ ├── dev/ │ └── test/ ├── metadata/ │ ├── metadata.csv │ ├── train.csv │ ├── dev.csv │ └── test.csv └── examples/

• Total reverberant files: 53,200
• Total duration: ~117.5 hours
• Underlying RIR pool: ~10,000 simulated RIRs (via RIR-Mega)

Splits

Train, development, and test splits are speaker-stratified to prevent speaker overlap across partitions. Acoustic parameters are not explicitly stratified, resulting in uneven coverage across the RT60–DRR plane.

Metadata Schema

Column	Description
audio	Relative path to reverberant WAV file
rt60	Reverberation time (seconds)
drr	Direct-to-reverberant ratio (dB)
c50	Clarity index C50 (dB)
lufs	Integrated loudness (LUFS)
duration_s	Utterance duration (seconds)
clean_id	Source LibriSpeech utterance ID
rir_id	Identifier of the RIR-Mega impulse response
split	Dataset split (train / dev / test)

Recommended Use

• Robust ASR benchmarking under controlled reverberation
• Dereverberation and speech enhancement evaluation
• Analysis of ASR error trends versus acoustic parameters

Limitations

• RIRs are simulated and may not capture all real-room effects (e.g., furniture, HVAC noise).
• Acoustic coverage across RT60 and DRR is uneven due to non-stratified sampling.
• The DRR definition differs from perceptual conventions that include early reflections.

Reproducibility

The underlying RIR corpus, dataset generation scripts, and evaluation pipeline are publicly available. Users can regenerate the dataset or construct alternative speech corpora using the same RIR pool.

License

• LibriSpeech: CC BY 4.0
• RIR-Mega RIRs: MIT
• Derived reverberant speech: CC BY 4.0

Citation

If you use this dataset, please cite both the speech corpus and the underlying RIR collection:

RIR-Mega-Speech

Goswami, M. (2026). RIR-Mega-Speech: A Reverberant Speech Corpus with Comprehensive Acoustic Metadata and Reproducible Evaluation. arXiv:2601.19949. https://arxiv.org/abs/2601.19949v1

RIR-Mega

Goswami, M. (2025). RIR-Mega: A Large-Scale Room Impulse Response Corpus with Benchmarks for Industrial and Building Acoustics. arXiv:2510.18917. https://arxiv.org/abs/2510.18917