The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Unsupervised Cross-Lingual Representation Learning for Speech Recognition
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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 · Cited by 73,173
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
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
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
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
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
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
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
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.
Cited by
Sorted by citations
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
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
Self-citationFLAVA: A Foundational Language And Vision Alignment Model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., & Kiela, D. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01519 · Cited by 678
A comprehensive survey on applications of transformers for deep learning tasks
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 580
Self-supervised speech representation learning: A review
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
“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 383
WhisperX: Time-Accurate Speech Transcription of Long-Form Audio
Bain, M., Huh, J., Han, T., & Zisserman, A. (2023) · INTERSPEECH 2023 · doi:10.21437/interspeech.2023-78 · Cited by 337
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningPreprint
Gunel, B., Du, J., Conneau, A., & Stoyanov, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01403 · Cited by 483
Self-citationLayer-Wise Analysis of a Self-Supervised Speech Representation Model
Pasad, A., Chou, J.-C., & Livescu, K. (2021) · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru51503.2021.9688093 · Cited by 262
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022
Saeki, T., Xin, D., Nakata, W., Koriyama, T., Takamichi, S., & Saruwatari, H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-439 · Cited by 251
FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech
Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V., Dalmia, S., Riesa, J., Rivera, C. E., & Bapna, A. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10023141 · Cited by 430
Self-citationUnsupervised Speech RecognitionPreprint
Baevski, A., Hsu, W.-N., Conneau, A., & Auli, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11084 · Cited by 207
Self-citationRobust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
Hsu, W.-N., Sriram, A., Baevski, A., Likhomanenko, T., Xu, Q., Pratap, V., Kahn, J., Lee, A., Collobert, R., Synnaeve, G., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-236 · Cited by 249
Self-citationWENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition
Zhang, B., Lv, H., Guo, P., Shao, Q., Yang, C., Xie, L., Xu, X., Bu, H., Chen, X., Zeng, C., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746682 · Cited by 156
A Literature Review of Textual Hate Speech Detection Methods and Datasets
Alkomah, F., & Ma, X. (2022) · Information · doi:10.3390/info13060273 · Cited by 167
Using transformers for multimodal emotion recognition: Taxonomies and state of the art review
Hazmoune, S., & Bougamouza, F. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108339 · Cited by 136
Luna-Jiménez, C., Kleinlein, R., Griol, D., Callejas, Z., Montero, J. M., & Fernández-Martínez, F. (2021) · Applied Sciences · doi:10.3390/app12010327 · Cited by 134
Prompting Large Language Models with Speech Recognition Abilities
Fathullah, Y., Wu, C., Lakomkin, E., Jia, J., Shangguan, Y., Li, K., Guo, J., Xiong, W., Mahadeokar, J., Kalinli, O., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447605 · Cited by 162
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models
Li, X., Wang, C., Tang, Y., Tran, C., Tang, Y., Pino, J., Baevski, A., Conneau, A., & Auli, M. (2021) · doi:10.18653/v1/2021.acl-long.68 · Cited by 112
Self-citationComparative Layer-Wise Analysis of Self-Supervised Speech Models
Pasad, A., Shi, B., & Livescu, K. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096149 · Cited by 105
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised RepresentationsPreprint
Choi, H.-S., Lee, J., Kim, W., Lee, J. H., Heo, H., & Lee, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14513 · Cited by 101
Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview
Bell, P., Fainberg, J., Klejch, O., Li, J., Renals, S. J., & Świętojański, P. (2020) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2020.3045349 · Cited by 97
Findings of the IWSLT 2022 Evaluation Campaign
Anastasopoulos, A., Barrault, L., Bentivogli, L., Boito, M. Z., Bojar, O., Cattoni, R., Currey, A., Dinu, G., Duh, K., Elbayad, M., et al. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.10 · Cited by 82
Zhao, J. S., & Zhang, W.-Q. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3184480 · Cited by 84
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled DataPreprint
Wang, C., Wu, Y., Qian, Y., Kumatani, K., Liu, S., Wei, F., Zeng, M., & Huang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07597 · Cited by 76
Simple and Effective Zero-shot Cross-lingual Phoneme Recognition
Xu, Q., Baevski, A., & Auli, M. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-60 · Cited by 115
Self-citationEvain, S. V., Nguyen, H., Le, H., Boito, M. Z., Mdhaffar, S., Alisamir, S., Tong, Z., Tomashenko, N., Dinarelli, M., Parcollet, T., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-556 · Cited by 78
Exploiting Adapters for Cross-Lingual Low-Resource Speech Recognition
Hou, W., Zhu, H., Wang, Y., Wang, J., Qin, T., Xu, R., & Shinozaki, T. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3138674 · Cited by 68
Towards End-to-End Unsupervised Speech Recognition
Liu, A. H., Hsu, W.-N., Auli, M., & Baevski, A. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10023187 · Cited by 85
Self-citationAudio Deepfake Detection: What Has Been Achieved and What Lies Ahead
Zhang, B., Cui, H., Nguyen, V., & Whitty, M. (2025) · Sensors · doi:10.3390/s25071989 · Cited by 72
Towards Building ASR Systems for the Next Billion Users
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 61
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 69
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
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
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 95
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 58
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
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 51
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 53
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 53
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
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 72
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 60
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 55
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 45
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 41
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
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 54
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 42
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 40
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 40
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 82
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 65
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
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 40
Vásquez-Correa, J. C., & Álvarez, A. (2023) · Sensors · doi:10.3390/s23041843 · Cited by 42
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 40
Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy
Eberhardt, S. T., Schaffrath, J., Moggia, D., Schwartz, B., Jaehde, M., Rubel, J. A., Baur, T., André, E., & Lutz, W. (2024) · Psychotherapy Research · doi:10.1080/10503307.2024.2322522 · Cited by 38
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 48
Torre, I. G., Romero, M. P., & Álvarez, A. (2021) · Applied Sciences · doi:10.3390/app11198872 · Cited by 33
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
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
Javanmardi, F., Kadiri, S. R., & Alku, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3392829 · Cited by 33
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 34
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 47
Nowakowski, K., Ptaszynski, M. E., Murasaki, K., & Nieuważny, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103148 · Cited by 32
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
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
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
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
Improved Meta Learning for Low Resource Speech Recognition
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
Performance-Efficiency Trade-Offs in Unsupervised Pre-Training for Speech Recognition
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 32
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 32
Efficient Self-Supervised Learning Representations for Spoken Language Identification
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
Improving Massively Multilingual ASR with Auxiliary CTC Objectives
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 32
wav2vec2-based Speech Rating System for Children with Speech Sound Disorder
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 27
Self-Supervised Learning of Audio Representations From Permutations With Differentiable Ranking
Carr, A. N., Berthet, Q., Blondel, M., Teboul, O., & Zeghidour, N. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3067635 · Cited by 26
Multilingual and Cross-Lingual Intent Detection from Spoken Data
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 41
A Step Towards Preserving Speakers’ Identity While Detecting Depression Via Speaker Disentanglement
Ravi, V., Wang, J., Flint, J., & Alwan, A. A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10798 · Cited by 26
Detecting hate speech in memes: a review
Hermida, P., & Santos, E. M. D. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10459-7 · Cited by 32
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
Cai, Z., & Li, M. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101597 · Cited by 30
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 26
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 29
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 42
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
Rekimoto, J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580706 · Cited by 28
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 25
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 23
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 23
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 26
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 26
Deschamps-Berger, T., Lamel, L. F., & Devillers, L. Y. (2022) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3536220.3558038 · Cited by 23
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 22
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
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
Wang, P., & hamme, H. V. (2023) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-023-00280-z · Cited by 22
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
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 22
Hussain, N., Qasim, A., Mehak, G., Zain, M., Sidorov, G., Gelbukh, A. F., & Kolesnikova, O. (2025) · AI · doi:10.3390/ai6070157 · Cited by 26
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 20
Metadata
- 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
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
