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

TranSentence: speech-to-speech Translation via Language-Agnostic Sentence-Level Speech Encoding without Language-Parallel Data

Kim, S.-B., Lee, S.-H., & Lee, S. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447331 · Cited by 3

StoryTTS: A Highly Expressive Text-to-Speech Dataset with Rich Textual Expressiveness Annotations

Liu, S., Guo, Y., Chen, X., & Yu, K. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446023 · Cited by 3

Wav2vec-AD: Acoustic Unit Discovery Module-Integrated, Self-Supervised Contrastive Pre-training Approach for Speech Recognition

Yolwas, N., Sun, L., Li, X., Liu, Q., & Wang, Z. (2024) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-024-2738-8 · Cited by 3

Enhancing Speech-to-Text Transcription Accuracy for the Bahraini Dialect

Almahmood, A., Al-Ammal, H., & Albalooshi, F. A. (2024) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict64318.2024.10824280 · Cited by 3

A Large Dataset of Spontaneous Speech with the Accent Spoken in São Paulo for Automatic Speech Recognition Evaluation

Lima, R. D. S., Leal, S. E., Cândido, A., & Aluísio, S. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79029-4_3 · Cited by 3

Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling

Huang, S.-S., Huang, K.-P., Liu, A. T., & Lee, H.-Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890363 · Cited by 3

EmoSDS: Unified Emotionally Adaptive Spoken Dialogue System Using Self-Supervised Speech Representations

Lee, J., Sim, Y., Kim, J., & Suh, Y.-J. (2025) · Future Internet · doi:10.3390/fi17040143 · Cited by 3

Transformer-Based Approach for Solving Mathematical Problems Using Automatic Speech Recognition

Grgurević, A., & Babac, M. B. (2025) · IEEE Access · doi:10.1109/access.2025.3564121 · Cited by 5

Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch

Poncelet, J., & hamme, H. V. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688061 · Cited by 4

Exploring Speaker Age Estimation on Different Self-Supervised Learning Models

Truong, D.-T., Anh, T. T., & Siong, C. E. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9979878 · Cited by 3

Multi-level Fusion of Fisher Vector Encoded BERT and Wav2vec 2.0 Embeddings for Native Language Identification

Krebbers, D., Kaya, H., & Karpov, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20980-2_34 · Cited by 3

UM-DFKI Maltese Speech Translation

Williams, A., Abela, K., Kumar, R., Bär, M., Billinghurst, H., Micallef, K., Samin, A. M., DeMarco, A., van der Plas, L., & Borg, C. (2023) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2023.iwslt-1.41 · Cited by 3

GMU Systems for the IWSLT 2023 Dialect and Low-resource Speech Translation Tasks

Mbuya, J., & Anastasopoulos, A. (2023) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2023.iwslt-1.24 · Cited by 3

Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23

Tsiamas, I., Gállego, G. I., Fonollosa, J. A. R., & Costa‐jussà, M. R. (2023) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2023.iwslt-1.38 · Cited by 3

From ‘Snippet-lects’ to Doculects and Dialects: Leveraging Neural Representations of Speech for Placing Audio Signals in a Language Landscape

Guillaume, S., Wisniewski, G., & Michaud, A. (2023) · doi:10.21437/sigul.2023-7 · Cited by 5

Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers

Mohebbi, H., Chrupała, G., Zuidema, W. H., & Alishahi, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.513 · Cited by 3

Universal Cross-Lingual Data Generation for Low Resource ASR

Wang, W., & Qian, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3345150 · Cited by 4

Variational Gaussian Process Data Uncertainty

Wong, H. M. J., Zhang, H., & Chen, N. F. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389678 · Cited by 3

Speech Recognition Using Machine Learning Techniques

Mishra, A., Dhanda, N., Gupta, K. K., & Verma, R. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489508 · Cited by 3

State-of-the-Art Review on Recent Trends in Automatic Speech Recognition

Kandji, A. K., Ba, C., & Ndiaye, S. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-63999-9_11 · Cited by 5

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

Rangappa, P., Zuluaga-Gómez, J., Madikeri, S. R., Carofilis, A., Prakash, J., Burdisso, S., Kumar, S., Villatoro-Tello, E., Nigmatulina, I., Motlíček, P., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888138 · Cited by 3

A Two-Stage LoRA Strategy for Expanding Language Capabilities in Multilingual ASR Models

Kwok, C. Y., Liu, H., Yip, J. Q., Li, S., & Chng, E. S. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3578752 · Cited by 4

Applications of Artificial Intelligence for Cross-Language Intelligibility Assessment of Dysarthric Speech

Yeo, E., Liss, J., Berisha, V., & Mortensen, D. R. (2025) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2025_persp-25-00030 · Cited by 6

Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition

Pritzen, J. M., Gref, M., Zühlke, D., & Schmidt, C. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5ei3oi3tvk9n · Cited by 3

End-to-End Cross-Lingual Spoken Language Understanding Model with Multilingual Pretraining

Zhang, X., & He, L. J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-818 · Cited by 3

On Architectures and Training for Raw Waveform Feature Extraction in ASR

Vieting, P., Luscher, C., Michel, W., Schlüter, R., & Ney, H. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688123 · Cited by 3

The THUEE System Description for the IARPA OpenASR21 Challenge

Zhao, J., Wang, H., Li, J., Chai, S., Wang, G., Chen, G., & Zhang, W.-Q. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-269 · Cited by 4

Quantifying Language Variation Acoustically with Few Resources

Bartelds, M., & Wieling, M. B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.273 · Cited by 4

Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data

Duret, J., Estève, Y., & Parcollet, T. (2023) · ISCA Speech Synthesis Workshop (SSW) · doi:10.21437/ssw.2023-29 · Cited by 4

ON-TRAC Consortium Systems for the IWSLT 2023 Dialectal and Low-resource Speech Translation Tasks

Laurent, A., Gahbiche, S., Nguyen, H., Elleuch, H., Bougares, F., Thiol, A., Riguidel, H., Mdhaffar, S., Laperrière, G., Maison, L., et al. (2023) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2023.iwslt-1.18 · Cited by 3

Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP

(2023) · doi:10.18653/v1/2023.sigtyp-1 · Cited by 3

Singer Identity Representation Learning Using Self-Supervised TechniquesPreprint

Torres, B., Lattner, S., & Gaël Richard, A. (2023) · arXiv (Cornell University) · doi:10.5281/zenodo.10265322 · Cited by 6

Direct articulatory observation reveals phoneme recognition performance characteristics of a self-supervised speech model

Shi, X., Feng, T., Huang, K., Kadiri, S. R., Lee, J., Lu, Y., Zhang, Y., Goldstein, L. M., & Narayanan, S. S. (2024) · JASA Express Letters · doi:10.1121/10.0034430 · Cited by 4

Adapting Large-Scale Pre-trained Models for Unified Dialect Speech Recognition Model

Toyama, T., Kai, A., Kamiya, Y., & TAKAHASHI, N. (2024) · Acta Physica Polonica A · doi:10.12693/aphyspola.146.413 · Cited by 3

Enhancing Stuttering Detection and Classification Using Wav2Vec2.0

Sen, M., & Das, P. K. (2024) · International Conference on Artificial Intelligence and Signal Processing (AISP) · doi:10.1109/aisp61711.2024.10870651 · Cited by 2

Automatic speech recognition in the presence of babble noise and reverberation compared to human speech intelligibility in Spanish

Mena, C. D. H., Padilla-Ortíz, A. L., & Orduña-Bustamante, F. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101856 · Cited by 2

Data stream-pairwise bottleneck transformer for engagement estimation from video conversation

Suzuki, K., Hojo, N., Shinoda, K., Mizuno, S., & Masumura, R. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1516295 · Cited by 2

Detecting depression through speech and text from casual talks with fully automated virtual humans

Gómez-Zaragozá, L., Altozano, A., Llanes-Jurado, J., Minissi, M. E., Alcañíz, M., & Marín‐Morales, J. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103305 · Cited by 2

Sentiment Analysis of Tokopedia Customer Reviews Using BiLSTM and IndoBERT with Comparative Analysis of Preprocessing and Labeling Methods

Anadra, R., Wijayanto, H., & Sadik, K. (2025) · International Journal of Advances in Data and Information Systems · doi:10.59395/ijadis.v6i3.1458 · Cited by 7

A Review of Question-Answering Systems Using Deep Learning in the Arabic Language

Aloqla, A., Khalifa, R. M. S., & Alghamdi, W. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.14229 · Cited by 3

The Development of Northern Thai Dialect Speech Recognition System

Bootkrajang, J., Inkeaw, P., Chaijaruwanich, J., Taerungruang, S., Boonyawisit, A., Sutawong, B. J. M., Chunwijitra, V., & Taninpong, P. (2025) · Applied Sciences · doi:10.3390/app16010160 · Cited by 3

Error-Resilient Semantic Communication for Speech Transmission Over Packet-Loss Networks

Han, Z., Dai, J., Yao, S., Wang, J., Li, Y., Niu, K., Xu, W., & Zhang, P. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3673476 · Cited by 3

AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models

Yang, C.-K., Ho, N., Lee, Y., & Lee, H.-Y. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434723 · Cited by 18

Domain Generalization for Language-Independent Automatic Speech Recognition

Gao, H., Ni, J., Zhang, Y., Qian, K., Chang, S., & Hasegawa‐Johnson, M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.806274 · Cited by 2

Deep Spectro-temporal Artifacts for Detecting Synthesized Speech

Liu, X., Liu, M., Zhang, L., Zhang, L., Zeng, C., Li, K., Li, N., Lee, K. A., Wang, L., & Dang, J. (2022) · doi:10.1145/3552466.3556527 · Cited by 4

Detecting Alzheimer’s Disease Based on Acoustic Features Extracted from Pre-trained Models

Mei, K., Guo, Z., Liu, Z., Liu, L., Li, X. S., & Ling, Z.-H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20503-3_22 · Cited by 2

Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models

Ashihara, T., Moriya, T., Matsuura, K., & Tanaka, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096318 · Cited by 3

A Framework for Automatic Generation of Spoken Question-Answering Data

Menevşe, M. Ü., Manav, Y., Arısoy, E., & Özgür, A. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.342 · Cited by 2

Comparing Self-Supervised Pre-Training and Semi-Supervised Training for Speech Recognition in Languages with Weak Language Models

Lam-Yee-Mui, L.-M., Ondel, L., & Klejch, O. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1802 · Cited by 3

ASR pipeline for low-resourced languages: A case study on Pomak

Tsoukala, C., Kritsis, K., Douros, I., Katsamanis, A., Kokkas, N., Arampatzakis, V., Sevetlidis, V., Μαρκαντωνάτου, Σ., & Pavlidis, G. P. (2023) · Workshop on NLP Applications to Field Linguistics (Field Matters) · doi:10.18653/v1/2023.fieldmatters-1.5 · Cited by 2

Compressing Wav2vec2 for Embedded Applications

Ludwig, O., & Claes, T. (2023) · Machine learning for signal processing ... · doi:10.1109/mlsp55844.2023.10285964 · Cited by 2

Comparing The Fine-Tuning and Performance of Whisper Pre-Trained Models for Turkish Speech Recognition Task

Oyucu, S. (2023) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit58785.2023.10304891 · Cited by 2

Breaking Barriers: Can Multilingual Foundation Models Bridge the Gap in Cross-Language Speech Emotion Recognition?

Shoukat, M., Usama, M., Ali, H. S., & Latif, S. (2023) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams60348.2023.10375468 · Cited by 2

Evaluating Self-Supervised Speech Models on a Taiwanese Hokkien Corpus

Chou, Y.-H., Chang, K., Wu, M.-J., Ou, W., Bi, A. W.-H., Yang, C., Chen, B., Pai, R.-W., Yeh, P.-Y., Chiang, J.-P., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389734 · Cited by 2

Findings of the 2023 ML-Superb Challenge: Pre-Training And Evaluation Over More Languages And Beyond

Shi, J., Chen, W., Berrebbi, D., Wang, H.-H., Huang, W.-P., Hu, E.-P., Chuang, H.-L., Chang, X., Tang, Y., Li, S.-W. D., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389628 · Cited by 2

Self-supervised learning for Formosan speech representation and linguistic phylogeny

Hsieh, S., Tseng, Y.-H., Lian, R., & Wang, C.-W. (2024) · Frontiers in Language Sciences · doi:10.3389/flang.2024.1338684 · Cited by 2

Enhancing Sentiment Analysis in Amharic: Leveraging Transformer-Based Language Model for Low-Resource African Languages

Raychawdhary, N., Das, A. K., Bhattacharya, S., Dozier, G. V., & Seals, C. (2024) · IEEE SoutheastCon · doi:10.1109/southeastcon52093.2024.10500147 · Cited by 2

End-To-End deep neural models for Automatic Speech Recognition for Polish Language

Pondel-Sycz, K., Pietrzak, A. P., & Szymla, J. (2024) · International Journal of Electronics and Telecommunications · doi:10.24425/ijet.2024.149547 · Cited by 2

Unveiling the Linguistic Capabilities of a Self-Supervised Speech Model Through Cross-Lingual Benchmark and Layer- Wise Similarity Analysis

Ashihara, T., Delcroix, M., Ijima, Y., & Kashino, M. (2024) · IEEE Access · doi:10.1109/access.2024.3428364 · Cited by 3

Low-Resourced Phonetic and Prosodic Feature Estimation With Self-Supervised-Learning-based Acoustic Modeling

Kurihara, K., & Sano, M. (2024) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw62465.2024.10626112 · Cited by 2

A Mispronunciation-Based Voice-Omics Representation Framework for Screening Specific Language Impairments in Children

Bo, W., Rubino, M., & Xu, W. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00045 · Cited by 2

Emotion Recognition from Contextualized Speech Representations using Fine-tuned Transformers

Cioroiu, G., & Rădoi, A. (2024) · International Conference on Communications (COMM) · doi:10.1109/comm62355.2024.10741432 · Cited by 2

Hubert-LSTM: A Hybrid Model for Artificial Intelligence and Human Speech

Baias, A.-C. (2024) · Engineering World · doi:10.37394/232025.2024.6.17 · Cited by 2

Towards a Livvi-Karelian End-to-End ASR System

Kipyatkova, I. S., Kagirov, I., Dolgushin, M., & Родионова, А. П. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77961-9_4 · Cited by 4

The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings

Ljubešić, N., Rupnik, P., & Koržinek, D. (2024) · International Conference on Speech and Computer · doi:10.1007/978-3-031-77961-9_10 · Cited by 11

Analyzing code-switching scenarios in India’s diverse linguistic landscape using end-to-end ASR systems with VITB-HEBiC

Jain, P., & Bhowmick, A. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109978 · Cited by 2

Minimum effort adaptation of automatic speech recognition system in air traffic management

Bhattacharjee, M., Motlíček, P., Madikeri, S. R., Helmke, H., Ohneiser, O., Kleinert, M., & Ehr, H. (2025) · European journal of transport and infrastructure research · doi:10.59490/ejtir.2024.24.4.7531 · Cited by 2

Combining multilingual resources to enhance end-to-end speech recognition systems for Scandinavian languages

Matějů, L., Nouza, J., Červa, P., & Žďánský, J. (2025) · Speech Communication · doi:10.1016/j.specom.2025.103221 · Cited by 2

Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Zhang, Y., Chen, Y., Wang, J., Hu, J., Tao, R., & Liu, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889070 · Cited by 2

Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis

Kim, M., Jang, K., & Kim, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888101 · Cited by 2

Leveraging distance information for generalized spoofing speech detection

Lu, J., Zhang, Y., Li, Z., Shang, Z., Wang, W., & Zhang, P. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101804 · Cited by 3

A Forced Decoding-based Approach for Enhancing Low-resource ASR

Liu, Y., Yang, X., & Qu, D. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11759-5 · Cited by 2

A Comprehensive Performance Evaluation of Whisper Models in Dysarthric Speech Recognition

Singh, S., Zhong, Z., Wang, Q., Mendes, C., Hasegawa‐Johnson, M., Abdulla, W. H., & Shahamiri, S. R. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6960-8_6 · Cited by 3

Transinger: Cross-Lingual Singing Voice Synthesis via IPA-Based Phonetic Alignment

Shen, C., Zhao, L., Fu, C., Gan, B., & Du, Z. (2025) · Sensors · doi:10.3390/s25133973 · Cited by 4

Post-training for Deepfake Speech Detection

Ge, W., Wang, X., Liu, X., & Yamagishi, J. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434709 · Cited by 25

Throat microphone speech recognition using wav2vec 2.0 and feature mapping

Masuda, K., Ogata, J., Nishida, M., & Nishimura, M. (2022) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce56475.2022.10014035 · Cited by 2

The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation

He, M., & Garner, P. N. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.291 · Cited by 2

Generalisation Gap of Keyword Spotters in a Cross-Speaker Low-Resource Scenario

Lepak, Ł., Radzikowski, K., Nowak, R. M., & Piczak, K. J. (2021) · Sensors · doi:10.3390/s21248313 · Cited by 2

Ensuring the Inclusive Use of NLP in the Global Response to COVID-19

Luccioni, A. S., Pham, K. H., Lam, C. S. N., Aylett-Bullock, J., & Luengo-Oroz, M. A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-93733-1_18 · Cited by 2

Plugging a neural phoneme recognizer into a simple language model: a workflow for low-resource setting

Guillaume, S., Wisniewski, G., Galliot, B., Nguyễn, M., Fily, M., Jacques, G., & Michaud, A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-11314 · Cited by 2

ERBFV-Net: An Emotion Recognizer Based on Face Video for MAHNOB-HCI

Kuang, H., Liu, J., Ma, X., & Liu, X. (2023) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec56291.2023.10082535 · Cited by 2

Effectiveness of Text, Acoustic, and Lattice-Based Representations in Spoken Language Understanding Tasks

Villatoro-Tello, E., Madikeri, S. R., Zuluaga-Gómez, J., Sharma, B., Sarfjoo, S. S., Nigmatulina, I., Motlíček, P., Ivanov, A. V., & Ganapathiraju, A. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095168 · Cited by 2

QUESPA Submission for the IWSLT 2023 Dialect and Low-resource Speech Translation Tasks

Ortega, J. E., Zevallos, R., & Chen, W. (2023) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2023.iwslt-1.23 · Cited by 2

Transformer Based Speech to Text Translation for Indic Languages

Satvik, A., Sistla, L., Kumar, R., & Kulkarni, K. A. (2023) · International Conference on Applied Intelligence and Sustainable Computing (ICAISC) · doi:10.1109/icaisc58445.2023.10200222 · Cited by 2

MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module

Plátek, O., & Dušek, O. (2023) · ISCA Speech Synthesis Workshop (SSW) · doi:10.21437/ssw.2023-8 · Cited by 2

Türkçe Konuşmadan Metne Dönüştürme için Ön Eğitimli Modellerin Performans Karşılaştırması: Whisper-Small ve Wav2Vec2-XLS-R-300M

Mercan, Ö. B., ÇEPNİ, S., Tasar, D. E., & Ozan, Ş. (2023) · Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi · doi:10.54525/tbbmd.1252487 · Cited by 4

Multilingual Models with Language Embeddings for Low-resource Speech Recognition

Lam-Yee-Mui, L.-M., Kheder, W. B., Le, V.-B., Barras, C., & Gauvain, J. (2023) · doi:10.21437/sigul.2023-18 · Cited by 4

Learning to Translate Low-Resourced Swiss German Dialectal Speech into Standard German Text

Khosravani, A., Garner, P. N., & Lazaridis, A. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688249 · Cited by 2

Automatically Identifying Language Family from Acoustic Examples in Low Resource ScenariosPreprint

Wu, P., Zhong, Y., & Black, A. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00876 · Cited by 2

PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images

Demir, K. C., Weise, T., May, M. S., Schmid, A., Maier, A., & Yang, S. H. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-753 · Cited by 1

Automated speech recognition of Indonesian-English language lessons on YouTube using transfer learning

Maxwell-Smith, Z., & Foley, B. (2023) · Workshop on NLP Applications to Field Linguistics (Field Matters) · doi:10.18653/v1/2023.fieldmatters-1.1 · Cited by 1

Speech Command Recognition Using Self-Supervised ASR in Tamil

Tharmakulasingham, I., & Thayasivam, U. (2023) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon60487.2023.10355402 · Cited by 1

Blendshape-Based Migratable Speech-Driven 3D Facial Animation with Overlapping Chunking-Transformer

Chen, J., Ma, X., Wang, L., & Cheng, J. S. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8432-9_4 · Cited by 1

Speech-Based Screening of Multiple Sclerosis By Features Derived from Self-Supervised Models

Gosztolya, G., & Egas-López, J. V. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389218 · Cited by 3

A Comparative Study on using English ASR Models for Leveraging Medical Speech Recognition System

Sasikala, D., & Sathya, S. S. (2023) · International Conference on Advances in Computational Intelligence and Communication (ICACIC) · doi:10.1109/icacic59454.2023.10434950 · Cited by 1

Towards Building a Language-Independent Speech Scoring Assessment

Gupta, S., Unnam, A., Yadav, K. S., & Aggarwal, V. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30366 · Cited by 1

Efficiency-oriented approaches for self-supervised speech representation learning

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Text-Independent Phone-to-Audio Alignment Leveraging SSL (TIPAA-SSL) Pre-Trained Model Latent Representation and Knowledge Transfer

Tits, N., Bhatnagar, P., & Dutoit, T. (2024) · Acoustics · doi:10.3390/acoustics6030042 · Cited by 1

Systematic Literature Review and Bibliometric Analysis of Low-Resource Speech-to-Text Translation

Kponou, D. F., Laleye, F. A. A., & Ezin, E. C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-73125-9_24 · Cited by 1

Wav2vec2 Without Attention: Do You Need Hopfield Networks for Self-Supervised Learning of Speech Representations?

Grebenkin, D., & Bondarenko, I. (2024) · Journal of Mathematical Sciences · doi:10.1007/s10958-024-07420-6 · Cited by 1

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

Discrete vs Continuous Audio Token Representations in Cross-Lingual Transfer Accuracy on CommonVoice Low-Resource Benchmark

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

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

DistilXLSR: A Light Weight Cross-Lingual Speech Representation Model

Wang, H., Wang, S., Zhang, W.-Q., & Bai, J. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1444 · Cited by 2

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

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