Unsupervised Cross-Lingual Representation Learning for Speech Recognition
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This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.
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XLSR supports 53 languages and XLS-R supports 128 languages.
“Depending on the number of languages used for training, XLSR supports 53 languages (XLSR [124]) and 128 languages (XLS-R [125]).”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [124]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Momentum Contrast for Unsupervised Visual Representation LearningPreprint
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. B. (2019) · Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1911.05722 · Cited by 16,250 · see citing works
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637 · see citing works
Self-citationGraves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370 · see citing works
Categorical Reparameterization with Gumbel-Softmax
Jang, E., Gu, S., & Poole, B. (2016) · arXiv · doi:10.48550/arxiv.1611.01144 · Cited by 3,616 · see citing works
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-4009 · Cited by 3,538 · see citing works
Self-citationProduct Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 1× · see citing works
“The quantization is based on product quantization (Jegou et al., 2011; Baevski et al., 2020b) by choosing quantized representations from G = 2 codebooks with V = 320 entries each.”
Methodwav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Baevski, A., Zhou, H., Mohamed, A., & Auli, M. (2020) · arXiv · doi:10.48550/arxiv.2006.11477 · Cited by 2,369 · see citing works
Self-citationCross-lingual Language Model Pretraining
Lample, G., & Conneau, A. (2019) · arXiv · doi:10.48550/arxiv.1901.07291 · Cited by 1,586 · see citing works
Self-citationwav2vec: Unsupervised Pre-Training for Speech Recognition
Schneider, S., Baevski, A., Collobert, R., & Auli, M. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-1873 · Cited by 1,290 · see citing works
Self-citationCommon Voice: A Massively-Multilingual Speech Corpus
Ardila, R., Branson, M., Davis, K., Henretty, M., Kohler, M., Meyer, J., Morais, R., Saunders, L., Tyers, F. M., & Weber, G. (2019) · arXiv · doi:10.48550/arxiv.1912.06670 · Cited by 905 · see citing works
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers
Huang, J.-T., Li, J., Yu, D., Deng, L., & Gong, Y. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639081 · Cited by 612 · see citing works
Scalable Modified Kneser-Ney Language Model Estimation
Heafield, K., Pouzyrevsky, I., Clark, J. H., & Koehn, P. (2013) · Cited by 442 · see citing works
vq-wav2vec: Self-Supervised Learning of Discrete Speech RepresentationsPreprint
Baevski, A., Schneider, S., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05453 · Cited by 431 · see citing works
Self-citationwav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. (2020) · neural information processing systems · Cited by 386 · see citing works
Self-citationCCNet: Extracting High Quality Monolingual Datasets from Web Crawl DataPreprint
Wenzek, G., Lachaux, M.-A., Conneau, A., Chaudhary, V., Guzmán, F., Joulin, A., & Grave, É. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.00359 · Cited by 362 · see citing works
Self-citationMLS: A Large-Scale Multilingual Dataset for Speech Research
Pratap, V., Xu, Q., Sriram, A., Synnaeve, G., & Collobert, R. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2826 · Cited by 361 · see citing works
Self-citationAn Unsupervised Autoregressive Model for Speech Representation Learning
Chung, Y.-A., Hsu, W.-N., Tang, H., & Glass, J. R. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1473 · Cited by 356 · see citing works
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges
Arivazhagan, N., Bapna, A., Firat, O., Lepikhin, D., Johnson, M., Krikun, M., Chen, M. X., Cao, Y., Foster, G., Cherry, C., et al. (2019) · arXiv · doi:10.48550/arxiv.1907.05019 · Cited by 279 · see citing works
Multilingual acoustic models using distributed deep neural networks
Heigold, G., Vanhoucke, V., Senior, A., Nguyen, P., Ranzato, M., Devin, M., & Dean, J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639348 · Cited by 277 · see citing works
Multilingual Speech Recognition with a Single End-to-End Model
Toshniwal, S., Sainath, T. N., Weiss, R. J., Li, B., Moreno, P. J., Weinstein, E., & Rao, K. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461972 · Cited by 216 · see citing works
Multilingual training of deep neural networks
Ghoshal, A. K., Świętojański, P., & Renals, S. J. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639084 · Cited by 197 · see citing works
Unsupervised Pretraining Transfers Well Across Languages
Riviere, M., Joulin, A., Mazare, P.-E., & Dupoux, E. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054548 · Cited by 186 · see citing works
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
Chung, Y.-A., & Glass, J. R. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2341 · Cited by 163 · see citing works
Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models
Burget, L., Schwarz, P., Agarwal, M., Akyazi, P., Feng, K., Ghoshal, A. K., Glembek, O., Goel, N. K., Karafiát, M., Povey, D., et al. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495646 · Cited by 162 · see citing works
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Kannan, A., Datta, A., Sainath, T. N., Weinstein, E., Ramabhadran, B., Wu, Y., Bapna, A., Chen, Z., & Lee, S. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2858 · Cited by 159 · see citing works
Transformers with convolutional context for ASRPreprint
Mohamed, A., Okhonko, D., & Zettlemoyer, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11660 · Cited by 151 · see citing works
Self-citationSpeech recognition and keyword spotting for low-resource languages : Babel project research at CUED
Gales, M., Knill, K., Ragni, A., & Rath, S. P. (2014) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 123 · see citing works
The challenge of realistic music generation: modelling raw audio at scalePreprint
Dieleman, S., van den Oord, A., & Simonyan, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10474 · Cited by 115 · see citing works
Cho, J., Baskar, M. K., Li, R., Wiesner, M., Mallidi, S. H., Yalta, N., Karafiát, M., Watanabe, S., & Hori, T. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639655 · Cited by 113 · see citing works
Towards language independent acoustic modeling
Byrne, B., Beyerlein, P., Huerta, J. M., Khudanpur, S. P., Marthi, B., Morgan, J., Peterek, N., Picone, J., Vergyri, D., & Wang, T. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.859138 · Cited by 109 · see citing works
Effectiveness of self-supervised pre-training for speech recognitionPreprint
Baevski, A., Auli, M., & Mohamed, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03912 · Cited by 105 · see citing works
Self-citationImproving Transformer-based Speech Recognition Using Unsupervised Pre-trainingPreprint
Jiang, D., Lei, X., Li, W., Luo, N., Hu, Y., Zou, W., & Li, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09932 · Cited by 94 · see citing works
Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language
Le, V.-B., & Besacier, L. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2009.2021723 · Cited by 84 · see citing works
VQVAE Unsupervised Unit Discovery and Multi-Scale Code2Spec Inverter for Zerospeech Challenge 2019
Tjandra, A., Şişman, B., Zhang, M., Sakti, S., Li, H., & Nakamura, S. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3232 · Cited by 65 · see citing works
Multilingually trained bottleneck features in spoken language recognition
Fér, R., Matějka, P., Grézl, F., Plchot, O., Veselý, K., & Černocký, J. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.06.008 · Cited by 59 · see citing works
Eloff, R., Nortje, A., van Niekerk, B., Govender, A., Nortje, L., Pretorius, A., van Biljon, E., van der Westhuizen, E., van Staden, L., & Kamper, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1518 · Cited by 54 · see citing works
An End-to-End Language-Tracking Speech Recognizer for Mixed-Language Speech
Seki, H., Watanabe, S., Hori, T., Roux, J. L., & Hershey, J. R. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462180 · Cited by 51 · see citing works
Transfer Learning of Language-independent End-to-end ASR with Language Model Fusion
Inaguma, H., Cho, J., Baskar, M. K., Kawahara, T., & Watanabe, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682918 · Cited by 47 · see citing works
An Unsupervised Autoregressive Model for Speech Representation LearningPreprint
Chung, Y.-A., Hsu, W.-N., Tang, H., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03240 · Cited by 45 · see citing works
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from SpeechPreprint
Chung, Y.-A., & Glass, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08976 · Cited by 34 · see citing works
The 2016 BBN Georgian telephone speech keyword spotting system
Alumäe, T., Karakos, D., Hartmann, W., Hsiao, R. W.-H., Zhang, L., Nguyen, L. H. B., Tsakalidis, S., & Schwartz, R. M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953259 · Cited by 30 · see citing works
Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks
Ragni, A., Li, Q., Gales, M., & Wang, Y. (2018) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.17863/cam.35236 · Cited by 20 · see citing works
Learning Robust and Multilingual Speech RepresentationsPreprint
Kawakami, K., Wang, L., Dyer, C., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.11128 · Cited by 13 · see citing works
Semi-supervised training in low-resource ASR and KWS
Metze, F., Gandhe, A., Miao, Y., Sheikh, Z., Wang, Y., Xu, D., Zhang, H., Kim, J., Lane, I. R., Lee, W. K., et al. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178862 · Cited by 12 · see citing works
VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019Preprint
Tjandra, A., Şişman, B., Zhang, M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11449 · Cited by 11 · see citing works
Ragni, A., Wu, C., Gales, M., Vasilakes, J., & Knill, K. M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953074 · Cited by 8 · see citing works
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded SpeechPreprint
Harwath, D. F., Hsu, W.-N., & Glass, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09602 · Cited by 8 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 2
- Mark JF Gales, Kate M Knill, and Anton Ragni. Low-resource speech recognition and keyword- spotting. In Proc. of SPECOM, pp. 3-19. Springer, 2017.
- Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. arXiv, 2019.
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Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y., Pino, J., et al. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-143 · Cited by 586 · see citing works
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Islam, S., Elmekki, H., Elsebai, A., Bentahar, J., Drawel, N., Rjoub, G., & Witold Pedrycz, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122666 · Cited by 522 · see citing works
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Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3207050 · Cited by 372 · see citing works
“In [282], a wav2vec 2.0 model was trained on data from multiple different languages and different corpora (Babel, Common Voice, and multilingual LS) jointly, followed by fine-tuning for each individual language.”
MethodInfluentialSelf-citationWang, C., Rivière, M., Lee, A., Wu, A., Talnikar, C., Haziza, D., Williamson, M., Pino, J., & Emmanuel Dupoux, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.80 · Cited by 339 · see citing works
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Javed, T., Doddapaneni, S., Raman, A., Bhogale, K. S., Ramesh, G., Kunchukuttan, A., Kumar, P., & Khapra, M. M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21327 · Cited by 58 · see citing works
Scaling End-to-End Models for Large-Scale Multilingual ASR
Li, B., Pang, R., Sainath, T. N., Gulati, A., Zhang, Y., Qin, J., Haghani, P., Huang, W., Ma, M., & Bai, J. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687871 · Cited by 56 · see citing works
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition
Hernandez, A., Pérez-Toro, P. A., Nöth, E., Orozco‐Arroyave, J. R., Maier, A., & Yang, S. H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10674 · Cited by 57 · see citing works
CDPAM: Contrastive Learning for Perceptual Audio Similarity
Manocha, P., Jin, Z., Zhang, R., & Finkelstein, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413711 · Cited by 56 · see citing works
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-TrainingPreprint
Bapna, A., Chung, Y.-A., Wu, N., Gulati, A., Ye, J., Clark, J. H., Johnson, M., Riesa, J., Conneau, A., & Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10329 · Cited by 64 · see citing works
Self-citationA generalizable speech emotion recognition model reveals depression and remission
Hansen, L., Zhang, Y., Wolf, D., Sechidis, K., Ladegaard, N., & Fusaroli, R. (2021) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13388 · Cited by 56 · see citing works
Orosoo, M., Raash, N., Treve, M., Lahza, H. F. M., Alshammry, N., Ramesh, J. V. N., & Rengarajan, M. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.10.065 · Cited by 51 · see citing works
ESPnet-SLU: Advancing Spoken Language Understanding Through ESPnet
Arora, S., Dalmia, S., Denisov, P., Chang, X., Ueda, Y., Peng, Y., Zhang, Y., Kumar, S., Ganesan, K., Yan, B., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747674 · Cited by 50 · see citing works
The MuSe 2022 Multimodal Sentiment Analysis Challenge
Christ, L., Amiriparian, S., Baird, A., Tzirakis, P., Kathan, A., Müller, N., Stappen, L., Meßner, E.-M., Konig, A. S., Cowen, A., et al. (2022) · doi:10.1145/3551876.3554817 · Cited by 48 · see citing works
Improving hate speech detection using Cross-Lingual Learning
Firmino, A. A., Baptista, C. D. S., & de Paiva, A. C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121115 · Cited by 52 · see citing works
Wang, H., Jin, Z., Geng, M., Hu, S., Li, G., Wang, T., Xu, H., & Liu, X. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447702 · Cited by 47 · see citing works
Indigenous peoples and artificial intelligence: A systematic review and future directions
Perera, M., Vidanaarachchi, R., Chandrashekeran, S., Kennedy, M., Kennedy, B., & Halgamuge, S. K. (2025) · Big Data & Society · doi:10.1177/20539517251349170 · Cited by 65 · see citing works
Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition
Winata, G. I., Wang, G., Xiong, C., & Hoi, S. C. H. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1390 · Cited by 48 · see citing works
Multilingual semantic distance: Automatic verbal creativity assessment in many languages.
Patterson, J. D., Merseal, H. M., Johnson, D. R., Agnoli, S., Baas, M., Baker, B. S., Barbot, B., Benedek, M., Borhani, K., Chen, Q., et al. (2023) · Psychology of Aesthetics Creativity and the Arts · doi:10.1037/aca0000618 · Cited by 46 · see citing works
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech RecognitionPreprint
Lai, C.-I., Zhang, Y., Liu, A. H., Chang, S., Liao, Y.-L., Chuang, Y.-S., Qian, K., Khurana, S., Cox, D., & Glass, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05933 · Cited by 41 · see citing works
Joint Unsupervised and Supervised Training for Multilingual ASR
Bai, J., Li, B., Zhang, Y., Bapna, A., Siddhartha, N., Sim, K. C., & Sainath, T. N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746038 · Cited by 40 · see citing works
Exploring Self-Supervised Pre-Trained ASR Models for Dysarthric and Elderly Speech Recognition
Hu, S., Xie, X., Jin, Z., Geng, M., Wang, Y., Cui, M., Deng, J., Liu, X., & Meng, H. M. L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097275 · Cited by 39 · see citing works
BartSmiles: Generative Masked Language Models for Molecular Representations
Chilingaryan, G., Tamoyan, H., Tevosyan, A., Babayan, N., Hambardzumyan, K., Navoyan, Z. E., Aghajanyan, A., Khachatrian, H., & Khondkaryan, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00512 · Cited by 41 · see citing works
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-Resource Speech Recognition
Yi, C., Zhou, S., & Xu, B. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3071668 · Cited by 39 · see citing works
Improved Language Identification Through Cross-Lingual Self-Supervised Learning
Tjandra, A., Choudhury, D. G., Zhang, F., Singh, K., Conneau, A., Baevski, A., Sela, A., Saraf, Y., & Auli, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747667 · Cited by 39 · see citing works
Self-citationSelf-Supervised ASR Models and Features for Dysarthric and Elderly Speech Recognition
Hu, S., Xie, X., Geng, M., Jin, Z., Deng, J., Li, G., Wang, Y., Cui, M., Wang, T., Meng, H. M. L., et al. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3422839 · Cited by 38 · see citing works
Introduction to latent variable energy-based models: a path toward autonomous machine intelligence
Dawid, A., & LeCun, Y. (2024) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ad292b · Cited by 48 · see citing works
Razumovskaia, E., Glavaš, G., Majewska, O., Ponti, E. M., Korhonen, A., & Vulić, I. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13083 · Cited by 57 · see citing works
Findings of the VarDial Evaluation Campaign 2023
Aepli, N., Çöltekin, Ç., van der Goot, R., Jauhiainen, T., Kazzaz, M., Ljubešić, N., North, K., Plank, B., Scherrer, Y., & Zampieri, M. (2023) · Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial) · doi:10.18653/v1/2023.vardial-1.25 · Cited by 36 · see citing works
Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition
Violeta, L. P., Huang, W. C., & Toda, T. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10043 · Cited by 36 · see citing works
Vásquez-Correa, J. C., & Álvarez, A. (2023) · Sensors · doi:10.3390/s23041843 · Cited by 36 · see citing works
Wav2Seq: Pre-Training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Wu, F., Kim, K., Watanabe, S., Han, K. J., McDonald, R., Weinberger, K. Q., & Artzi, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096988 · Cited by 36 · see citing works
Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy
Eberhardt, S. T., Schaffrath, J., Danilo Moggia, N., Schwartz, B., Jaehde, M., Rubel, J. A., Baur, T., André, E., & Lutz, W. (2024) · Psychotherapy Research · doi:10.1080/10503307.2024.2322522 · Cited by 36 · see citing works
Masked Modeling Duo: Towards a Universal Audio Pre-Training Framework
Niizumi, D., Takeuchi, D., Ohishi, Y., Harada, N., & Kashino, K. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3389636 · Cited by 38 · see citing works
Torre, I. G., Romero, M. P., & Álvarez, A. (2021) · Applied Sciences · doi:10.3390/app11198872 · Cited by 33 · see citing works
A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers
Zuluaga-Gómez, J., Prasad, A., Nigmatulina, I., Motlíček, P., & Kleinert, M. (2023) · Aerospace · doi:10.3390/aerospace10050490 · Cited by 38 · see citing works
Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389648 · Cited by 33 · see citing works
Javanmardi, F., Kadiri, S. R., & Alku, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392829 · Cited by 33 · see citing works
Cheng, G., Wang, X., Cooper, E., Wells, D., Wang, L., Dang, J., Richmond, K., & Yamagishi, J. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3451951 · Cited by 31 · see citing works
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-Level Cross-Lingual Speech Representation
Khurana, S., Laurent, A., & Glass, J. R. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3192714 · Cited by 31 · see citing works
Nowakowski, K., Ptaszynski, M. E., Murasaki, K., & Nieuważny, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103148 · Cited by 30 · see citing works
What to remember: Self-adaptive continual learning for audio deepfake detection
Zhang, X., Yi, J., Wang, C., Zhang, C. Y., Zeng, S., & Tao, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29929 · Cited by 32 · see citing works
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language
Zhang, S., Frey, B. E., & Bansal, M. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.108 · Cited by 34 · see citing works
Wang, X., & Yamagishi, J. (2024) · ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp48485.2024.10446331 · Cited by 29 · see citing works
Huang, W.-C., Wu, Y.-C., & Hayashi, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9415079 · Cited by 30 · see citing works
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Singh, S., Wang, R., & Hou, F. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746899 · Cited by 29 · see citing works
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Wu, F., Kim, K., Pan, J. S., Han, K. J., Weinberger, K. Q., & Artzi, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747432 · Cited by 30 · see citing works
Neural representations for modeling variation in speech
Bartelds, M., de Vries, W., Sanal, F., Richter, C., Liberman, M., & Wieling, M. B. (2022) · Journal of Phonetics · doi:10.1016/j.wocn.2022.101137 · Cited by 31 · see citing works
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Liu, H., Garcia, L. P., Khong, A. W. H., Chng, E. S., Styles, S. J., & Khudanpur, S. P. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3201445 · Cited by 28 · see citing works
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Chen, W., Yan, B., Shi, J., Peng, Y., Maiti, S., & Watanabe, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095326 · Cited by 31 · see citing works
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Getman, Y., Al-Ghezi, R., Voskoboinik, K., Grósz, T., Kurimo, M., Salvi, G., Svendsen, T. K., & Strömbergsson, S. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10103 · Cited by 26 · see citing works
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Carr, A. N., Berthet, Q., Blondel, M., Teboul, O., & Zeghidour, N. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3067635 · Cited by 24 · see citing works
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Gerz, D., Su, P.-H., Kusztos, R., Mondal, A., Lis, M., Singhal, E., Mrkšić, N., Wen, T.-H., & Vulić, I. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.591 · Cited by 28 · see citing works
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Ravi, V., Wang, J., Flint, J., & Alwan, A. A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10798 · Cited by 26 · see citing works
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Hermida, P., & Santos, E. M. D. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10459-7 · Cited by 25 · see citing works
Jin, L., Oh, Y., Kim, H., Jung, H., Jon, H. J., Shin, J. E., & Kim, E. Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096253 · Cited by 25 · see citing works
Cai, Z., & Li, M. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101597 · Cited by 26 · see citing works
Luz, S. F., Haider, F., Fromm, D., Lazarou, I., Kompatsiaris, I. Y., & MacWhinney, B. (2024) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2024.3378595 · Cited by 24 · see citing works
Hou, R., Fütterer, T., Bühler, B., Bozkir, E., Gerjets, P. H., Trautwein, U., & Kasneci, E. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64302-6_5 · Cited by 25 · see citing works
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesPreprint
Gupta, A., Chadha, H. S., Shah, P., Chhimwal, N., Dhuriya, A., Gaur, R., & Raghavan, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07402 · Cited by 34 · see citing works
Real-Time End-to-End Speech Emotion Recognition with Cross-Domain Adaptation
Wongpatikaseree, K., Singkul, S., Hnoohom, N., & Yuenyong, S. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6030079 · Cited by 23 · see citing works
Rekimoto, J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580706 · Cited by 23 · see citing works
Yang, C.-H. H., Li, B., Zhang, Y., Chen, N., Prabhavalkar, R., Sainath, T. N., & Strohman, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094903 · Cited by 23 · see citing works
Temporal Knowledge Distillation for on-device Audio Classification
Choi, K., Kersner, M., Morton, J., & Chang, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747908 · Cited by 21 · see citing works
A Comparative Study of Self-Supervised Speech Representation Based Voice Conversion
Huang, W.-C., Yang, S.-W., Hayashi, T., & Toda, T. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3193761 · Cited by 22 · see citing works
Speech Technology for Automatic Recognition and Assessment of Dysarthric Speech: An Overview
Bhat, C. G., & Strik, H. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2024_jslhr-23-00740 · Cited by 21 · see citing works
Pretext Tasks Selection for Multitask Self-Supervised Audio Representation Learning
Zaiem, S., Parcollet, T., Essid, S., & Heba, A. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3195430 · Cited by 22 · see citing works
Deschamps-Berger, T., Lamel, L. F., & Devillers, L. Y. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536220.3558038 · Cited by 21 · see citing works
Advancing Naturalistic Affective Science with Deep Learning
Lin, C., Bulls, L. S., Tepfer, L. J., Vyas, A. D., & Thornton, M. A. (2023) · Affective Science · doi:10.1007/s42761-023-00215-z · Cited by 21 · see citing works
A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks
Qiu, Y., Fang, H., Yu, H., Chen, B., Meikang Qiu, A., & Xia, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_7 · Cited by 21 · see citing works
The Voicemos Challenge 2024: Beyond Speech Quality Prediction
Huang, W.-C., Fu, S., Cooper, E., Zezario, R. E., Toda, T., Wang, H., Yamagishi, J., & Tsao, Y. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832295 · Cited by 20 · see citing works
Wang, P., & hamme, H. V. (2023) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-023-00280-z · Cited by 20 · see citing works
Zhao, X., Wen, H., Xu, G., Pang, T., Zhang, Y., He, X., Hu, R., Yan, M., Chen, C., Wu, X., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13668 · Cited by 20 · see citing works
Generative Interpretable Visual Design: Using Disentanglement for Visual Conjoint Analysis
Sisodia, A., Burnap, A., & Kumar, V. (2024) · Journal of Marketing Research · doi:10.1177/00222437241276736 · Cited by 19 · see citing works
Hussain, N., Qasim, A., Mehak, G., Zain, M., Sidorov, G., Gelbukh, A. F., & Kolesnikova, O. (2025) · AI · doi:10.3390/ai6070157 · Cited by 24 · see citing works
Zheng, G., Xiao, Y., Gong, K., Zhou, P., Liang, X., & Lin, L. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.236 · Cited by 19 · see citing works
- Authors
- Conneau, A., Baevski, A., Collobert, R., Mohamed, A., & Auli, M.
- Affiliations
- Meta (United Kingdom) (GB)
- ORCID
- Auli, Michael
- Venue
- Interspeech 2021
- Publisher
- ISCA
- Published
- 2021-08-30
- Pages
- 2426-2430
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W3037057938
- Cited by
- 637
- References
- 47
- Citations by year
- 2026: 60 · 2025: 136 · 2024: 115 · 2023: 155 · 2022: 103 · 2021: 61 · 2020: 7
- FWCI
- 50.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- cross-lingual representation learning, multilingual speech recognition, XLS-R, wav2vec 2.0, contrastive learning, shared latent representation, Common Voice, Babel
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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