Kaldi Speech Recognition Toolkit

Untitled work

(2014) · Cited by 33,419

“Librispeech: an ASR corpus based on public domain audio books,”

Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558

Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

Chan, W., Jaitly, N., Le, Q., & Vinyals, O. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472621 · Cited by 2,226

Attention-Based Models for Speech Recognition

Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.07503 · Cited by 2,021

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621

High-performance brain-to-text communication via handwriting

Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2021) · Nature · doi:10.1038/s41586-021-03506-2 · Cited by 1,024

A time delay neural network architecture for efficient modeling of long temporal contexts

Peddinti, V., Povey, D., & Khudanpur, S. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-647 · Cited by 986

Speaker Recognition from Raw Waveform with SincNet

Ravanelli, M., & Bengio, Y. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639585 · Cited by 896

COVAREP — A collaborative voice analysis repository for speech technologies

Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014) · 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2014.6853739 · Cited by 695

ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection

Todisco, M., Wang, X., Vestman, V., Sahidullah, M., Delgado, H., Nautsch, A., Yamagishi, J., Evans, N., Kinnunen, T. H., & Lee, K. A. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2249 · Cited by 687

Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge

Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017) · Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3037697.3037698 · Cited by 673

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630

ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA

Han, S., Kang, J., Mao, H., Hu, Y., Li, X., Li, Y., Xie, D., Luo, H., Yao, S., Wang, Y., et al. (2017) · Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/3020078.3021745 · Cited by 591

Evaluating deep learning architectures for Speech Emotion Recognition

Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554

Attentive Statistics Pooling for Deep Speaker Embedding

Okabe, K., Koshinaka, T., & Shinoda, K. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-993 · Cited by 514

SpeechBrain: A General-Purpose Speech Toolkit

Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Dawalatabad, N., Heba, A., Zhong, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.04624 · Cited by 512

ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505

ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

Yamagishi, J., Wang, X., Todisco, M., Sahidullah, M., Patino, J., Nautsch, A., Liu, X., Lee, K. A., Kinnunen, T., Evans, N., et al. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-8 · Cited by 380

Towards Better Decoding and Language Model Integration in Sequence to Sequence Models

Chorowski, J., & Jaitly, N. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-343 · Cited by 358

Automatic Speech Recognition: A Survey

Malik, M., Malik, M. K., Mehmood, K., & Makhdoom, I. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10073-7 · Cited by 358

Deep learning in robotics: A review of recent research

Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331

An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model

Kannan, A., Wu, Y., Nguyen, P., Sainath, T. N., Chen, Z., & Prabhavalkar, R. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462682 · Cited by 253

Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models

Swietojanski, P., & Renals, S. (2014) · 2014 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078569 · Cited by 218

ASVspoof 2019: Spoofing Countermeasures for the Detection of Synthesized, Converted and Replayed Speech

Nautsch, A., Wang, X., Evans, N., Kinnunen, T. H., Vestman, V., Todisco, M., Delgado, H., Sahidullah, M., Yamagishi, J., & Lee, K. A. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3059479 · Cited by 185

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182

BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition

Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3182537 · Cited by 161

Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection

Tao, R., Pan, Z., Das, R. K., Qian, X., Shou, M. Z., & Li, H. (2021) · arXiv · doi:10.48550/arxiv.2107.06592 · Cited by 156

Parallel training of DNNs with Natural Gradient and Parameter Averaging

Povey, D., Zhang, X., & Khudanpur, S. (2014) · arXiv · doi:10.48550/arxiv.1410.7455 · Cited by 148

Machine learning and natural language processing in psychotherapy research: alliance as example use case.

Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology · doi:10.1037/cou0000382 · Cited by 141

Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation

Swietojanski, P., Li, J., & Renals, S. (2016) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2016.2560534 · Cited by 133

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity

Angrick, M., Ottenhoff, M. C., Diener, L., Ivucic, D., Ivucic, G., Goulis, S., Saal, J., Colon, A. J., Wagner, L., Krusienski, D. J., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02578-0 · Cited by 125

Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals

Kinnunen, T., Delgado, H., Evans, N., Lee, K. A., Vestman, V., Nautsch, A., Todisco, M., Wang, X., Sahidullah, M., Yamagishi, J., et al. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2020.3009494 · Cited by 108

The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance

Zhang, L., Wang, X., Cooper, E., Evans, N., & Yamagishi, J. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2022.3233236 · Cited by 100

Automatic quantitative analysis of spontaneous aphasic speech

Le, D., Licata, K., & Mower Provost, E. (2018) · Speech Communication · doi:10.1016/j.specom.2018.04.001 · Cited by 97

Recent Progress in the CUHK Dysarthric Speech Recognition System

Liu, S., Geng, M., Hu, S., Xie, X., Cui, M., Yu, J., Liu, X., & Meng, H. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3091805 · Cited by 96

Deep Variational Canonical Correlation Analysis

Wang, W., Yan, X., Lee, H., & Livescu, K. (2016) · arXiv · doi:10.48550/arxiv.1610.03454 · Cited by 89

14.4 A scalable speech recognizer with deep-neural-network acoustic models and voice-activated power gating

Price, M., Glass, J., & Chandrakasan, A. P. (2017) · 2017 IEEE International Solid-State Circuits Conference (ISSCC) · doi:10.1109/isscc.2017.7870352 · Cited by 86

Dementia detection using automatic analysis of conversations

Mirheidari, B., Blackburn, D., Walker, T., Reuber, M., & Christensen, H. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.07.006 · Cited by 82

SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network

Chan, W., Park, D., Lee, C., Zhang, Y., Le, Q., & Norouzi, M. (2021) · arXiv · doi:10.48550/arxiv.2104.02133 · Cited by 79

Revisiting Hidden Markov Models for Speech Emotion Recognition

Mao, S., Tao, D., Zhang, G., Ching, P. C., & Lee, T. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683172 · Cited by 83

JustSpeak: enabling universal voice control on Android

Zhong, Y., Raman, T. V., Burkhardt, C., Biadsy, F., & Bigham, J. P. (2014) · Proceedings of the 11th Web for All Conference · doi:10.1145/2596695.2596720 · Cited by 70

Audio-Visual Deep Neural Network for Robust Person Verification

Qian, Y., Chen, Z., & Wang, S. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3057230 · Cited by 63

Exploring ASR-free end-to-end modeling to improve spoken language understanding in a cloud-based dialog system,

Qian, Y., Ubale, R., Ramanaryanan, V., Lange, P., Suendermann-Oeft, D., Evanini, K., & Tsuprun, E. (2017) · 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru.2017.8268987 · Cited by 62

An automated medical scribe for documenting clinical encounters

Finley, G., Edwards, E., Robinson, A., Brenndoerfer, M., Sadoughi, N., Fone, J., Axtmann, N., Miller, M., & Suendermann-Oeft, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n18-5003 · Cited by 61

Automation of Language Sample Analysis

Liu, H., MacWhinney, B., Fromm, D., & Lanzi, A. (2023) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-22-00642 · Cited by 59

A Computational Future for Preventing HIV in Minority Communities: How Advanced Technology Can Improve Implementation of Effective Programs

Brown, C. H., Mohr, D. C., Gallo, C. G., Mader, C., Palinkas, L., Wingood, G., Prado, G., Kellam, S. G., Pantin, H., Poduska, J., et al. (2013) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0b013e31829372bd · Cited by 55

Study of Pre-Processing Defenses Against Adversarial Attacks on State-of-the-Art Speaker Recognition Systems

Joshi, S., Villalba, J., Zelasko, P., Moro-Velazquez, L., & Dehak, N. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3116438 · Cited by 53

Group sparse representation with wavenet vocoder adaptation for spectrum and prosody conversion

Sisman, B., Zhang, M., & Li, H. (2019) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2910637 · Cited by 52

Mariana: tencent deep learning platform and its applications

Zou, Y., Jin, X., Li, Y., Guo, Z., Wang, E., & Xiao, B. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2733004.2733082 · Cited by 49

Safety First: Conversational Agents for Health Care

Bickmore, T., Trinh, H., Asadi, R., & Olafsson, S. (2018) · Human–Computer Interaction Series · doi:10.1007/978-3-319-95579-7_3 · Cited by 50

Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition

Geng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48

The Effect of the MFCC Frame Length in Automatic Voice Pathology Detection

Tirronen, S., Kadiri, S. R., & Alku, P. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2022.03.021 · Cited by 45

Automatic speech recognition for acoustical analysis and assessment of cantonese pathological voice and speech

Lee, T., Liu, Y., Huang, P.-W., Chien, J.-T., Lam, W. K., Yeung, Y. T., Law, T. K. T., Lee, K. Y. S., Kong, A. P.-H., & Law, S.-P. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472924 · Cited by 44

An Initial Investigation for Detecting Partially Spoofed Audio

Zhang, L., Wang, X., Cooper, E., Yamagishi, J., Patino, J., & Evans, N. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-738 · Cited by 43

On the Limit of English Conversational Speech Recognition

Tüske, Z., Saon, G., & Kingsbury, B. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-211 · Cited by 41

Computational Cognitive Assessment: Investigating the Use of an Intelligent Virtual Agent for the Detection of Early Signs of Dementia

Mirheidari, B., Blackburn, D., OrMalley, R., Walker, T., Venneri, A., Reuber, M., & Christensen, H. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682423 · Cited by 34

CSR: A Cloud-Assisted Speech Recognition Service for Personal Mobile Device

Chang, Y.-S., Hung, S.-H., Wang, N. J. C., & Lin, B.-S. (2011) · 2011 International Conference on Parallel Processing · doi:10.1109/icpp.2011.23 · Cited by 34

Automatic Speech Recognition With Very Large Conversational Finnish and Estonian Vocabularies

Enarvi, S., Smit, P., Virpioja, S., & Kurimo, M. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2743344 · Cited by 33

Automatic Assessment of Speech Impairment in Cantonese-Speaking People with Aphasia

Qin, Y., Lee, T., & Kong, A. P. H. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2956371 · Cited by 32

TheanoLM — An Extensible Toolkit for Neural Network Language Modeling

Enarvi, S., & Kurimo, M. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-618 · Cited by 28

Exploiting ultrasound tongue imaging for the automatic detection of speech articulation errors

Ribeiro, M. S., Cleland, J., Eshky, A., Richmond, K., & Renals, S. (2021) · Speech Communication · doi:10.1016/j.specom.2021.02.001 · Cited by 27

Diagnosing People with Dementia Using Automatic Conversation Analysis

Mirheidari, B., Blackburn, D., Reuber, M., Walker, T., & Christensen, H. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-857 · Cited by 25

PriMock57: A Dataset Of Primary Care Mock Consultations

Papadopoulos Korfiatis, A., Moramarco, F., Sarac, R., & Savkov, A. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.65 · Cited by 23

Talk2Me: Automated linguistic data collection for personal assessment

Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv · doi:10.48550/arxiv.2012.14966 · Cited by 21

Contrastive Predictive Coding Based Feature for Automatic Speaker Verification

Lai, C.-I. (2019) · arXiv · doi:10.48550/arxiv.1904.01575 · Cited by 20

Recognition of Dysarthric Speech Using Voice Parameters for Speaker Adaptation and Multi-Taper Spectral Estimation

Bhat, C., Vachhani, B., & Kopparapu, S. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-1085 · Cited by 20

Untitled work

(2016) · doi:10.1007/s10579-016-9336-9 · Cited by 19

Text-Free Prosody-Aware Generative Spoken Language Modeling

Kharitonov, E., Lee, A., Polyak, A., Adi, Y., Copet, J., Lakhotia, K., Nguyen, T.-A., Rivière, M., Mohamed, A., Dupoux, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2109.03264 · Cited by 16

Automatic detection of phonological errors in child speech using siamese recurrent autoencoder

Ng, S.-I., & Lee, T. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2145 · Cited by 14

Reproducible Speech Research With the Artificial Intelligence–Ready PERCEPT Corpora

Benway, N. R., Preston, J. L., Hitchcock, E., Rose, Y., Salekin, A., Liang, W., & McAllister, T. (2023) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-22-00343 · Cited by 12

Exploiting Cross Domain Acoustic-to-Articulatory Inverted Features for Disordered Speech Recognition

Hu, S., Liu, S., Xie, X., Geng, M., Wang, T., Hu, S., Cui, M., Liu, X., & Meng, H. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746989 · Cited by 14

Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform

Steiner, I., & Le Maguer, S. (2018) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/2s9w7jk4ompu · Cited by 11

“Computer, Test My Hearing”: Accurate Speech Audiometry with Smart Speakers

Ooster, J., Moreta, P. N. P., Bach, J.-H., Holube, I., & Meyer, B. T. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2118 · Cited by 10

Towards automatic assessment of aphasia speech using automatic speech recognition techniques

Qin, Y., Lee, T., Kong, A. P. H., & Law, S. P. (2016) · 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp.2016.7918445 · Cited by 7

Using Automatic Speech Recognition to Measure the Intelligibility of Speech Synthesized From Brain Signals

Varshney, S., Farias, D., Brandman, D. M., Stavisky, S. D., & Miller, L. M. (2023) · 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER) · doi:10.1109/ner52421.2023.10123751 · Cited by 6

Neural Architecture Search for LF-MMI Trained Time Delay Neural Networks

Hu, S., Xie, X., Liu, S., Cui, M., Geng, M., Liu, X., & Meng, H. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413630 · Cited by 6

With One Voice: Composing a Travel Voice Assistant from Repurposed Models

Poran, S., Amsalem, G., Beka, A., & Goldenberg, D. (2022) · Companion Proceedings of the Web Conference 2022 · doi:10.1145/3487553.3524228 · Cited by 4

Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild

Halpern, B. M., Son, R. V., Brekel, M. V. D., & Scharenborg, O. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1598 · Cited by 4