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VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation

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arxiv 2101.00390 v2 pith:SUXYLW2A submitted 2021-01-02 cs.CL eess.AS

Changhan Wang , Morgane Rivière , Ann Lee , Anne Wu , Chaitanya Talnikar , Daniel Haziza , Mary Williamson , Juan Pino
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Emmanuel Dupoux
This is my paper
classification cs.CLeess.AS
keywords learningvoxpopulicorpusdatahourslanguagessemi-supervisedspeech
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised representation learning as well as semi-supervised learning. VoxPopuli also contains 1.8K hours of transcribed speeches in 16 languages and their aligned oral interpretations into 5 other languages totaling 5.1K hours. We provide speech recognition baselines and validate the versatility of VoxPopuli unlabelled data in semi-supervised learning under challenging out-of-domain settings. We will release the corpus at https://github.com/facebookresearch/voxpopuli under an open license.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SimulS2ST-Omni: Data-Efficient Streaming Speech-to-Speech Translation via Explicit Trajectory Supervision

    cs.SD 2026-07 conditional novelty 6.0 of 10

    A joint text-code trajectory supervision recipe lets a two-stream speech LM achieve competitive long-form streaming S2ST with ~2k hours of paired speech.

  2. ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis

    cs.SD 2025-10 conditional novelty 6.0 of 10

    ParsVoice is an open ~1,800–2,200-hour Persian audiobook-derived speech-text corpus, much larger than prior open Persian TTS datasets.

  3. From perception to production: how acoustic invariance facilitates articulatory learning in a self-supervised vocal imitation model

    eess.AS 2025-09 conditional novelty 6.0 of 10

    Intermediate wav2vec 2.0 layers provide the best self-supervised speech representations for learning articulatory gestures and imitating speech across speakers.

  4. On Barriers to Archival Audio Processing

    cs.SD 2025-07 conditional novelty 6.0 of 10

    On archival radio audio, Whisper V3 identifies languages with 91.3% accuracy, but speaker embeddings lose similarity across ages and languages, making speaker recognition unreliable for indexing.

  5. Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A benchmark of eight post-training quantization methods on Whisper and Moonshine edge speech models across seven datasets, finding 8-bit is safe and 3-bit weights are viable for larger models with advanced methods like SpQR.

  6. A Semi-spontaneous Dutch Speech Dataset for Speech Enhancement and Speech Recognition

    eess.AS 2026-03 conditional novelty 5.5 of 10

    On real noisy Dutch semi-spontaneous speech, five of eight SOTA ASR models reach WER under 22%, yet five single-channel SE methods fail to improve and often degrade ASR.

  7. FastSLM: Hierarchical Temporal Abstraction for Efficient Long-Form Speech Adaptation

    eess.AS 2026-01 conditional novelty 5.0 of 10

    A hierarchical Q-Former compresses speech to about 1.67 tokens/sec, enabling hour-long audio processing with near-linear memory scaling and competitive benchmark scores.

  8. Group Relative Policy Optimization for Speech Recognition

    eess.AS 2025-09 conditional novelty 5.0 of 10

    Applying GRPO with rule-based rewards to LLM-based ASR improves WER by up to 18.4% relative and reduces hallucination errors on unseen acoustic conditions.

  9. SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings

    cs.CL 2025-08 conditional novelty 5.0 of 10

    A parameter-efficient adapter bridging Whisper and TinyLlama reports relative improvements in speech recognition, named entity recognition, and sentiment analysis on low-resource benchmarks.

  10. An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications

    eess.AS 2025-07 reject novelty 5.0 of 10

    The paper introduces AER, an LLM-judged question-answering metric for evaluating ASR output in LLM applications, and shows it does not correlate strongly with WER.

  11. Unified Semi-Supervised Pipeline for Automatic Speech Recognition

    eess.AS 2025-06 conditional novelty 5.0 of 10

    A new semi-supervised ASR framework, TopIPL, combines a dynamic pseudo-label cache and top-N checkpoint teacher averaging to improve WER by up to 40 percent in low-resource settings.

  12. Instituto de Telecomunica\c{c}\~oes at IWSLT 2025: Aligning Small-Scale Speech and Language Models for Speech-to-Text Learning

    cs.CL 2025-06 conditional novelty 4.0 of 10

    The IT-IST IWSLT 2025 submission shows that a 1.5B language model with a speech encoder can do reasonable ASR after alignment, but struggles with ST and SQA.

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