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Less is More: Accurate Speech Recognition & Translation without Web-Scale Data

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arxiv 2406.19674 v1 pith:RZFDABDC submitted 2024-06-28 cs.CL cs.LGcs.SDeess.AS

Less is More: Accurate Speech Recognition & Translation without Web-Scale Data

Krishna C. Puvvada , Piotr \.Zelasko , He Huang , Oleksii Hrinchuk , Nithin Rao Koluguri , Kunal Dhawan , Somshubra Majumdar , Elena Rastorgueva
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Zhehuai Chen Vitaly Lavrukhin Jagadeesh Balam Boris Ginsburg
This is my paper
classification cs.CL cs.LGcs.SDeess.AS
keywords dataspeechtranslationmodeltrainingdynamiclessmodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Recent advances in speech recognition and translation rely on hundreds of thousands of hours of Internet speech data. We argue that state-of-the art accuracy can be reached without relying on web-scale data. Canary - multilingual ASR and speech translation model, outperforms current state-of-the-art models - Whisper, OWSM, and Seamless-M4T on English, French, Spanish, and German languages, while being trained on an order of magnitude less data than these models. Three key factors enables such data-efficient model: (1) a FastConformer-based attention encoder-decoder architecture (2) training on synthetic data generated with machine translation and (3) advanced training techniques: data-balancing, dynamic data blending, dynamic bucketing and noise-robust fine-tuning. The model, weights, and training code will be open-sourced.

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Cited by 4 Pith papers

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  2. BlasBench: An Open Benchmark for Irish Speech Recognition

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    BlasBench supplies an Irish-aware normalizer and scoring harness that enables reproducible ASR comparisons and exposes a 33-43 point generalization gap for fine-tuned models versus 7-10 points for massively multilingual ones.

  3. LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness

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    LCS-CTC, a phoneme recognizer trained with similarity-aware LCS alignment masks constraining CTC, outperforms vanilla CTC on all reported PER, WPER, boundary-loss, and articulatory metrics.

  4. Frame-Aligned Fusion of Canary and WavLM for Non-Intrusive Intelligibility Prediction of Hearing-Aid-Processed Speech

    eess.AS 2026-05 unverdicted novelty 4.0

    Frame-aligned fusion of Canary and WavLM encoders, with WavLM temporally prepared via learnable strided convolution, outperforms other fusion strategies and reaches Eval RMSE 24.96 and Corr 0.796 on non-intrusive inte...