Unsupervised Cross-Lingual Representation Learning for Speech Recognition

Attention Is All You Need

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

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Graves, 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

Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson’s Disease Detection and Speech Features Extraction

Klempíř, O., & Krupička, R. (2024) · Sensors · doi:10.3390/s24175520 · Cited by 23

Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages

San, N., Bartelds, M., Browne, M., Clifford, L., Gibson, F., Mansfield, J., Nash, D., Simpson, J., Turpin, M., Vollmer, M., et al. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688301 · Cited by 27

Self-Supervised Pre-Training for Attention-Based Encoder-Decoder ASR Model

Gao, C., Cheng, G., Ta, L., Zhang, P., & Yan, Y. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3171967 · Cited by 18

Improvement of Accent Classification Models Through Grad-Transfer From Spectrograms and Gradient-Weighted Class Activation Mapping

Carofilis, A., Alegre, E., Fidalgo, E., & Fernández-Robles, L. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3297961 · Cited by 19

XTREME-S: Evaluating Cross-lingual Speech Representations

Conneau, A., Bapna, A., Zhang, Y., Ma, M., von Platen, P., Lozhkov, A., Cherry, C., Ye, J., Rivera, C., Kale, M., et al. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10007 · Cited by 29

Collaborative AI Dysarthric Speech Recognition System With Data Augmentation Using Generative Adversarial Neural Network

He, Y., Seng, K. P., & Ang, L.-M. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3570383 · Cited by 21

Transfer Ability of Monolingual Wav2vec2.0 for Low-resource Speech Recognition

Cheng, Y., Wang, J., Cheng, N., Zhou, S., & Xu, B. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533587 · Cited by 17

Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview

Du, W., Maimaitiyiming, Y., Nijat, M., Li, L., Hamdulla, A., & Wang, D. (2022) · Applied Sciences · doi:10.3390/app13010326 · Cited by 22

Explore the World of Audio Deepfakes: A Guide to Detection Techniques for Non-Experts

Schäfer, K., Choi, J., & Zmudzinski, S. (2024) · ACM International Workshop on Multimedia AI against Disinformation (MAD) · doi:10.1145/3643491.3660289 · Cited by 21

Recent Advances in Discrete Speech Tokens: A Review

Guo, Y., Li, Z., Wang, H., Li, B., Shao, C., Zhang, H., Du, C., Xie, C., Liu, S., & Yu, K. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3643619 · Cited by 58

Pseudo-Labeling for Massively Multilingual Speech Recognition

Lugosch, L., Likhomanenko, T., Synnaeve, G., & Collobert, R. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746832 · Cited by 19

Language Adaptive Cross-Lingual Speech Representation Learning with Sparse Sharing Sub-Networks

Lu, Y., Huang, M., Qu, X., Wei, P., & Ma, Z. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747671 · Cited by 16

EmoFace: Audio-driven Emotional 3D Face Animation

Liu, C., Lin, Q., Zeng, Z., & Pan, Y. (2024) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr58804.2024.00060 · Cited by 16

Cross-Lingual Self-training to Learn Multilingual Representation for Low-Resource Speech Recognition

Zhang, Z., Song, Y., Wu, M., Fang, X., McLoughlin, I. V., & Dai, L.-R. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-02075-7 · Cited by 35

Improving Language Identification of Accented Speech

Kukk, K., & Alumäe, T. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10455 · Cited by 17

LeBenchmark 2.0: A standardized, replicable and enhanced framework for self-supervised representations of French speech

Parcollet, T., Nguyen, H., Evain, S. V., Boito, M. Z., Pupier, A., Mdhaffar, S., Le, H., Alisamir, S., Tomashenko, N., Dinarelli, M., et al. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101622 · Cited by 37

Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach

Takashima, R., Sawa, Y., Aihara, R., Takiguchi, T., & Imai, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3374874 · Cited by 15

A comprehensive survey with critical analysis for deepfake speech detection

Pham, L. D., Lam, P., Tran, D., Tang, H., Nguyen, T., Schindler, A., Skopik, F., Polonsky, A., & Vu, H. C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100757 · Cited by 28

A Comparison of Self-Supervised Speech Representations As Input Features For Unsupervised Acoustic Word Embeddings

van Staden, L., & Kamper, H. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383625 · Cited by 17

Multilingual Text-To-Speech Training Using Cross Language Voice Conversion And Self-Supervised Learning Of Speech Representations

Wu, J., Polyak, A., Taigman, Y., Fong, J., Agrawal, P., & He, Q. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746282 · Cited by 15

Voice Conversion Can Improve ASR in Very Low-Resource Settings

Baas, M., & Kamper, H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-112 · Cited by 18

WavLM model ensemble for audio deepfake detection

Combei, D., Stan, A., Oneata, D., & Cucu, H. (2024) · The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024) · doi:10.21437/asvspoof.2024-25 · Cited by 15

End-to-End Automated Speech Recognition Using a Character Based Small Scale Transformer Architecture

Loubser, A., de Villiers, P., & Freitas, A. D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124119 · Cited by 21

Self-Supervised End-to-End ASR for Low Resource L2 Swedish

Al-Ghezi, R., Getman, Y., Rouhe, A., Hildén, R., & Kurimo, M. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1710 · Cited by 19

Attention-Guided Adaptation for Code-Switching Speech Recognition

Aditya, B., Rohmatillah, M., Tai, L.-H., & Chien, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446258 · Cited by 14

Unsupervised contrastive learning: Shale porosity prediction based on conventional well logging

Qiao, L., Yang, S., Hu, Q., Wang, H., & He, T. (2024) · Physics of Fluids · doi:10.1063/5.0206449 · Cited by 14

Comparing CTC and LFMMI for Out-of-Domain Adaptation of wav2vec 2.0 Acoustic Model

Vyas, A., Madikeri, S. R., & Bourlard, H. A. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1683 · Cited by 15

Continual-wav2vec2: an Application of Continual Learning for Self-Supervised Automatic Speech Recognition.Preprint

Kessler, S., Thomas, B., & Karout, S. A. (2021) · arXiv (Cornell University) · Cited by 15

Comparative performance analysis of end-to-end ASR models on Indo-Aryan and Dravidian languages within India’s linguistic landscape

Jain, P., & Bhowmick, A. (2025) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-025-00395-5 · Cited by 13

Kazakh Speech Recognition: Wav2vec2.0 vs. Whisper

Kozhirbayev, Z. (2023) · Journal of Advances in Information Technology · doi:10.12720/jait.14.6.1382-1389 · Cited by 17

MECOS: A bilingual Manipuri–English spontaneous code-switching speech corpus for automatic speech recognition

Singh, N. K., Chanu, Y. J., & Pangsatabam, H. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101627 · Cited by 13

Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

Nabende, J. N., Babirye, C., Nabende, P., Tusubira, J., Mukiibi, J., Wairagala, E. P., Mutebi, C., Bateesa, T. S., Nahabwe, A., Tusiime, H., et al. (2024) · Applied AI Letters · doi:10.1002/ail2.92 · Cited by 13

Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation

Chen, T., Lin, J., Yang, Z., Qing, C., Shi, Y., & Lin, L. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02358-x · Cited by 13

Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation

Cheng, Y., Bapna, A., Fırat, O., Cao, Y. J., Wang, P., & Macherey, W. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.282 · Cited by 14

PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification

Liu, H., Garcia, L. P., Khong, A. W. H., Styles, S. J., & Khudanpur, S. P. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-354 · Cited by 13

Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using Wav2vec 2.0

Kunešová, M., & Zajíc, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094972 · Cited by 14

Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

Laptev, A., Andrusenko, A., Podluzhny, I., Mitrofanov, A., Medennikov, I., & Matveev, Y. N. (2021) · Sensors · doi:10.3390/s21093063 · Cited by 13

Using big data from long-form recordings to study development and optimize societal impact

Cychosz, M., & Cristià, A. (2022) · Advances in child development and behavior · doi:10.1016/bs.acdb.2021.12.001 · Cited by 13

Adaptation Algorithms for Speech Recognition: An OverviewPreprint

Bell, P., Fainberg, J., Klejch, O., Li, J., Renals, S. J., & Świętojański, P. (2020) · arXiv (Cornell University) · Cited by 12

Unsupervised Speaker Verification Using Pre-Trained Model and Label Correction

Chen, Z., Wang, J., Hu, W., Li, L., & Hong, Q. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094610 · Cited by 13

BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric

Chen, M., Duquenne, P.-A., Andrews, P., Kao, J., Mourachko, A., Schwenk, H., & Costa‐jussà, M. R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.504 · Cited by 21

Transfer Learning for Personality Perception via Speech Emotion Recognition

Li, Y., Bell, P., & Lai, C. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2061 · Cited by 12

Quantifying the Dialect Gap and its Correlates Across Languages

Kantharuban, A., Vulić, I., & Korhonen, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.481 · Cited by 22

AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR

Olatunji, T., Afonja, T., Yadavalli, A., Emezue, C. C., Singh, S., Dossou, B. F. P., Osuchukwu, J., Osei, S., Tonja, A. L., Etori, N. A., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00627 · Cited by 24

Multilingual Distilwhisper: Efficient Distillation of Multi-Task Speech Models Via Language-Specific Experts

Ferraz, T. P., Boito, M. Z., Brun, C., & Nikoulina, V. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447520 · Cited by 19

Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition

Saliba, A., Li, Y., Sanabria, R., & Lai, C. (2024) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw62465.2024.10627629 · Cited by 12

Comparison of wav2vec 2.0 models on three speech processing tasks

Kunešová, M., Zajíc, Z., Šmídl, L., & Karafiát, M. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10140-6 · Cited by 13

UniTalker: Scaling up Audio-Driven 3D Facial Animation Through A Unified Model

Fan, X., Li, J., Lin, Z., Xiao, W., & Yang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72940-9_12 · Cited by 12

Machine Learning Approaches for Speech-Based Alzheimer’s Detection: A Comprehensive Survey

Sharafeldeen, A., Keowen, J., & Shaffie, A. (2025) · Computers · doi:10.3390/computers14020036 · Cited by 19

A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems

Boito, M. Z., Besacier, L., Tomashenko, N., & Estève, Y. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-353 · Cited by 23

Cancelable biometric schemes for Euclidean metric and Cosine metric

Jiang, Y., Shen, P., Zeng, L., Zhu, X., Jiang, D. M., & Chen, C. (2023) · Cybersecurity · doi:10.1186/s42400-023-00137-0 · Cited by 12

Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-to-Speech

Saeki, T., Zen, H., Chen, Z., Morioka, N., Wang, G., Zhang, Y., Bapna, A., Rosenberg, A., & Ramabhadran, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095702 · Cited by 12

A Novel Online Quizzing System for Blind People by implementing Modern Voice Recognition Techniques

Kamra, V., Singh, A., Sharma, P., & Yadav, R. (2023) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon57910.2023.10127578 · Cited by 12

K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables

Kim, J., & Kang, P. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-547 · Cited by 15

Indonesian Automatic Speech Recognition with XLSR-53

Arisaputra, P., & Zahra, A. (2022) · Ingénierie des systèmes d information · doi:10.18280/isi.270614 · Cited by 18

End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions

Kang, W., Hasegawa‐Johnson, M., & Roy, D. K. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2298 · Cited by 13

Transduce and Speak: Neural Transducer for Text-To-Speech with Semantic Token Prediction

Kim, M., Jeong, M., Choi, B. J., Lee, D., & Kim, N. S. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389791 · Cited by 11

Magic Dust for Cross-Lingual Adaptation of Monolingual Wav2vec-2.0

Khurana, S., Laurent, A., & Glass, J. R. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746276 · Cited by 12

Prosody Modelling With Pre-Trained Cross-Utterance Representations for Improved Speech Synthesis

Zhang, Y.-J., Zhang, C., Song, W. G., Zhang, Z., Wu, Y., & He, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3278184 · Cited by 12

The Applicability of Wav2Vec2 and Whisper for Low-Resource Maltese ASR

Williams, A., DeMarco, A., & Borg, C. (2023) · doi:10.21437/sigul.2023-9 · Cited by 11

Learning ASR Pathways: A Sparse Multilingual ASR Model

Yang, M., Tjandra, A., Liu, C., Zhang, D., Le, D., & Kalinli, O. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094300 · Cited by 13

Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous SpeechPreprint

Qin, Y. H., Liu, W., Peng, Z., Ng, S.-I., Li, J., Hu, H., & Lee, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01493 · Cited by 17

T-Modules: Translation Modules for Zero-Shot Cross-Modal Machine Translation

Duquenne, P.-A., Gong, H., Sagot, B., & Schwenk, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.391 · Cited by 16

Multilingual Word Error Rate Estimation: E-Wer3

Chowdhury, S. A., & Ali, A. M. A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095888 · Cited by 13

CML-TTS: A Multilingual Dataset for Speech Synthesis in Low-Resource Languages

de Oliveira, F. S., Casanova, E., Cândido, A., Soares, A. S., & Filho, A. R. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_17 · Cited by 12

Multilingual end-to-end ASR for low-resource Turkic languages with common alphabets

Bekarystankyzy, A., Mamyrbayev, O. J., Mendes, M., Fazylzhanova, A., & Assam, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-64848-1 · Cited by 10

Spoken language identification: An overview of past and present research trends

O’Shaughnessy, D. D. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103167 · Cited by 10

ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks

Boito, M. Z., Ortega, J., Riguidel, H., Laurent, A., Barrault, L., Bougares, F., Chaabani, F., Nguyen, H., Barbier, F., Gahbiche, S., et al. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.28 · Cited by 15

Multilingual and Multimodal Abuse Detection

Sharon, R., Shah, H., Mukherjee, D., & Gupta, V. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10629 · Cited by 10

SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination

Yuan, Z., Wu, F., Long, Y., Xiao, C., & Li, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20065-6_9 · Cited by 11

Zero-Shot Normalization Driven Multi-Speaker Text to Speech Synthesis

Kumar, N., Narang, A., & Lall, B. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3169634 · Cited by 11

Fine-tuning pre-trained models for Automatic Speech Recognition, experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)

Guillaume, S., Wisniewski, G., Macaire, C., Jacques, G., Michaud, A., Galliot, B., Coavoux, M., Rossato, S., Nguyễn, M., & Fily, M. (2022) · Workshop on the Use of Computational Methods in the Study of Endangered Languages (ComputEL) · doi:10.18653/v1/2022.computel-1.21 · Cited by 12

Situating Automatic Speech Recognition Development within Communities of Under-heard Language Speakers

Reitmaier, T., Wallington, E., Klejch, O., Markl, N., Lam-Yee-Mui, L.-M., Pearson, J. S., Jones, M., Bell, P., & Robinson, S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581385 · Cited by 9

Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge

Alumäe, T., & Kukk, K. (2022) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2022-34 · Cited by 9

Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems: A Case Study for Modern Greek

Paraskevopoulos, G., Kouzelis, T., Rouvalis, G., Katsamanis, A., Katsouros, V., & Potamianos, A. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3328280 · Cited by 9

Seals_Lab at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages, Hausa and Igbo

Raychawdhary, N., Das, A. K., Dozier, G. V., & Seals, C. (2023) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2023.semeval-1.208 · Cited by 10

Zero Resource Code-Switched Speech Benchmark Using Speech Utterance Pairs for Multiple Spoken Languages

Huang, K.-P., Yang, C.-K., Fu, Y.-K., Dunbar, E., & Lee, H.-Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446737 · Cited by 9

UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition DatasetPreprint

Wang, C., Wu, Y., Liu, S., Li, J., Qian, Y., Kumatani, K., & Wei, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05233 · Cited by 14

ASR4REAL: An extended benchmark for speech modelsPreprint

Rivière, M., Copet, J., & Synnaeve, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08583 · Cited by 15

ITALIC: An Italian Intent Classification Dataset

Koudounas, A., Quatra, M. L., Vaiani, L., Colomba, L., Attanasio, G., Pastor, E., Cagliero, L., & Baralis, E. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1980 · Cited by 12

Automatic speech recognition (ASR) for the diagnosis of pronunciation of speech sound disorders in Korean children

Ahn, T., Hong, Y., Im, Y., Kim, D. H., Kang, D., Jeong, J. W., Kim, J. W., Kim, M. J., Cho, A., Nam, H., et al. (2024) · Clinical Linguistics & Phonetics · doi:10.1080/02699206.2024.2387609 · Cited by 12

Automatic Parkinson’s disease detection from speech: Layer selection vs adaptation of foundation models

Purohit, T., Ruvolo, B., Orozco‐Arroyave, J. R., & Magimai.-Doss, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890852 · Cited by 11

Development of a Baby Cry Identification System Using a Raspberry Pi-Based Embedded System and Machine Learning

Mekhfioui, M., Fadel, W., Hammouch, F.-E., Laayati, O., Bouchouirbat, M., Bazi, N. E., Satif, A., Boujiha, T., & Chebak, A. (2025) · Technologies · doi:10.3390/technologies13040130 · Cited by 8

Advancements in Multilingual Speech Technologies for Human-Computer Interaction

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Bravo-Rocca, G., Liu, P., Guitart, J., Dholakia, A., Ellison, D. D., Falkanger, J., & Hodak, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117232 · Cited by 9

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Liu, C., Chan, Y.-H., Ho, M.-Y., Liu, C., Lu, M., Liao, Y.-A., Hsieh, M. H., & Tseng, Y. J. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf102 · Cited by 7

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Kernel, S. R. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20651351

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