WhisperX: Time-Accurate Speech Transcription of Long-Form Audio

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

“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

HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov, R., & Mohamed, A. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3122291 · Cited by 2,983

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Baevski, A., Zhou, H., Mohamed, A., & Auli, M. (2020) · arXiv · doi:10.48550/arxiv.2006.11477 · Cited by 2,369

Robust Speech Recognition via Large-Scale Weak Supervision

Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022) · arXiv · doi:10.48550/arxiv.2212.04356 · Cited by 1,953

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., Li, J., Kanda, N., Yoshioka, T., Xiao, X., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3188113 · Cited by 1,926

Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi

McAuliffe, M., Socolof, M., Mihuc, S., Wagner, M., & Sonderegger, M. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1386 · Cited by 1,169

HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips

Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00272 · Cited by 986

The AMI Meeting Corpus: A Pre-announcement

Carletta, J., Ashby, S., Bourban, S., Flynn, M., Guillemot, M., Hain, T., Kadlec, J., Vasilis Karaiskos, B., Kraaij, W., Kronenthal, M., et al. (2006) · Lecture notes in computer science · doi:10.1007/11677482_3 · Cited by 939

Unsupervised Cross-Lingual Representation Learning for Speech Recognition

Conneau, A., Baevski, A., Collobert, R., Mohamed, A., & Auli, M. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-329 · Cited by 637

Automatic Speech Recognition: A survey of deep learning techniques and approaches

Ahlawat, H., Aggarwal, N., & Gupta, D. (2025) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2024.12.007 · Cited by 86

Emilia: An Extensive, Multilingual, and Diverse Speech Dataset For Large-Scale Speech Generation

He, H., Shang, Z., Wang, C., Li, X., Gu, Y., Hua, H., Liu, L., Yang, C., Li, J., Shi, P., et al. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832365 · Cited by 141

Measuring the Accuracy of Automatic Speech Recognition Solutions

Kuhn, K., Kersken, V., Reuter, B., Egger, N., & Zimmermann, G. (2023) · ACM Transactions on Accessible Computing · doi:10.1145/3636513 · Cited by 44

Take the aTrain. Introducing an interface for the Accessible Transcription of Interviews

Haberl, A., Fleiß, J., Kowald, D., & Thalmann, S. (2024) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2024.100891 · Cited by 44

AutoAD: Movie Description in Context

Han, T., Bain, M., Nagrani, A., Varol, G., Xie, W., & Zisserman, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01815 · Cited by 43

Scaling Up Video Summarization Pretraining with Large Language Models

Argaw, D. M., Yoon, S., Heilbron, F. C., Deilamsalehy, H., Bui, T., Wang, Z., Dernoncourt, F., & Chung, J. S. (2024) · 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/cvpr52733.2024.00796 · Cited by 46

Natural language processing models reveal neural dynamics of human conversation

Cai, J., Hadjinicolaou, A. E., Paulk, A. C., Soper, D. J., Xia, T., Wang, A. F., Rolston, J. D., Richardson, R. M., Williams, Z. M., & Cash, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-58620-w · Cited by 47

AutoAD II: The Sequel – Who, When, and What in Movie Audio Description

Han, T., Bain, M., Nagrani, A., Varol, G., Xie, W., & Zisserman, A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01255 · Cited by 39

Real-time multilingual speech recognition and speaker diarization system based on Whisper segmentation

Lyu, K.-M., Lyu, R.-Y., & Chang, H.-T. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1973 · Cited by 26

LLMREI: Automating Requirements Elicitation Interviews with LLMs

Korn, A., Gorsch, S., & Vogelsang, A. (2025) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re63999.2025.00013 · Cited by 25

Retrieval-Augmented Egocentric Video Captioning

Xu, J., Huang, Y., Hou, J., Chen, G., Zhang, Y., Feng, R., & Xie, W. (2024) · 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/cvpr52733.2024.01284 · Cited by 42

Automatic recognition of second language speech-in-noise

Kim, S.-E., Chernyak, B. R., Seleznova, O., Keshet, J., Goldrick, M., & Bradlow, A. R. (2024) · JASA Express Letters · doi:10.1121/10.0024877 · Cited by 24

MM-Narrator: Narrating Long-form Videos with Multimodal In-Context Learning

Zhang, C., Lin, K., Yang, Z., Wang, J., Li, L., Lin, C.-C., Liu, Z., & Wang, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01295 · Cited by 24

AutoAD III: The Prequel - Back to the Pixels

Han, T., Bain, M., Nagrani, A., Varol, G., Xie, W., & Zisserman, A. (2024) · 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/cvpr52733.2024.01720 · Cited by 31

Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings

Favaro, A., Butala, A., Thebaud, T., Villalba, J., Dehak, N., & Moro-Velázquez, L. (2024) · npj Parkinson s Disease · doi:10.1038/s41531-024-00817-9 · Cited by 22

SpoofCeleb: Speech Deepfake Detection and SASV in the Wild

Jung, J.-W., Wu, Y., Wang, X., Kim, J.-H., Maiti, S., Matsunaga, Y., Shim, H.-J., Tian, J., Evans, N., Chung, J. S., et al. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3529377 · Cited by 26

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

Benchmarking open source and paid services for speech to text: an analysis of quality and input variety

Ferraro, A., Galli, A., Gatta, V. L., & Postiglione, M. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1210559 · Cited by 27

Towards decoding individual words from non-invasive brain recordings

d’Ascoli, S., Bel, C., Rapin, J., Banville, H. J., Benchetrit, Y., Pallier, C., & King, J.-R. (2025) · Nature Communications · doi:10.1038/s41467-025-65499-0 · Cited by 44

Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions

Eberhardt, S. T., Vehlen, A., Schaffrath, J., Schwartz, B., Baur, T., Schiller, D., Hallmen, T., André, E., & Lutz, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-14923-y · Cited by 23

Large Language Models for Psychological Assessment: A Comprehensive Overview

Brickman, J., Gupta, M., & Oltmanns, J. R. (2025) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251343582 · Cited by 24

AI-driven mock interview assessment: leveraging generative language models for automated evaluation

Uppalapati, P. J., Dabbiru, M., & Kasukurthi, V. R. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02529-9 · Cited by 17

PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings

Kalda, J., Pagès, C., Marxer, R., Alumäe, T., & Bredin, H. (2024) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2024-17 · Cited by 17

Relax: Composable Abstractions for End-to-End Dynamic Machine Learning

Lai, R., Shao, J., Feng, S. M., Lyubomirsky, S., Hou, B., Lin, W., Ye, Z., Jin, H., Jin, Y., Liu, J., et al. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3676641.3716249 · Cited by 17

What automatic speech recognition can and cannot do for conversational speech transcription

Russell, S. O., Gessinger, I., Krason, A., Vigliocco, G., & Harte, N. (2024) · Research Methods in Applied Linguistics · doi:10.1016/j.rmal.2024.100163 · Cited by 16

Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement

Funk, M., Okada, S., & André, E. (2024) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3678957.3685757 · Cited by 16

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care

Bauer, J. F., Gerczuk, M., Schindler‐Gmelch, L., Amiriparian, S., Ebert, D. D., Krajewski, J., Schuller, B. W., & Berking, M. (2024) · Depression and Anxiety · doi:10.1155/2024/9667377 · Cited by 17

Learning visual language models for video understanding

Yang, A. (2023) · theses.fr (ABES) · doi:10.70675/802872a7z8092z4556za1fdz2c7af45f0e72 · Cited by 26

Artificial Intelligence‐Generated Feedback for Second Language Intelligibility: An Exploratory Intervention Study on Effects and Perceptions

Hirschi, K., Kang, O., Mu, Y., Hansen, J. H. L., & Beloin, K. (2025) · Language Learning · doi:10.1111/lang.12719 · Cited by 14

HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer’s Disease Detection From Spontaneous Speech

Dong, Z., Zhang, Z., Xu, W., Han, J., Ou, J., & Schuller, B. W. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446795 · Cited by 15

AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description

Xie, J., Han, T., Bain, M., Nagrani, A., Varol, G., Xie, W., & Zisserman, A. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0908-6_5 · Cited by 16

Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses

Gómez-Zaragozá, L., Wills, S., Tejedor-Garcı́a, C., Marín‐Morales, J., Alcañíz, M., & Strik, H. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1734 · Cited by 13

Zero-Shot Domain-Sensitive Speech Recognition with Prompt-Conditioning Fine-Tuning

Liao, F.-T., Chan, Y.-C., Chen, Y., Hsu, C.-J., & Shiu, D.-S. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389617 · Cited by 16

PITCH: AI-assisted Tagging of Deepfake Audio Calls using Challenge-Response

Mittal, G., Jakobsson, A., Marshall, K. A., Hegde, C., & Memon, N. D. (2025) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3708821.3733861 · Cited by 12

Less Peaky and More Accurate CTC Forced Alignment by Label Priors

Huang, R., Zhang, X., Ni, Z., Sun, L., Hira, M., Hwang, J., Manohar, V., Pratap, V., Wiesner, M., Watanabe, S., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446111 · Cited by 11

LLM-based speaker diarization correction: A generalizable approach

Efstathiadis, G., Yadav, V., & Abbas, A. (2025) · Speech Communication · doi:10.1016/j.specom.2025.103224 · Cited by 12

Listen Closely: Measuring Vocal Tone in Corporate Disclosures

Ewertz, J., Knickrehm, C., Nienhaus, M., & Reichmann, D. (2025) · Journal of Accounting Research · doi:10.1111/1475-679x.70015 · Cited by 15

Analyzing embodied learning in classroom settings: A human-in-the-loop AI approach for multimodal learning analytics

Fonteles, J. H., Cohn, C., Ayalon, E., Zhou, M., Ashwin, T. S., Davalos, E., Li, Z., Rayala, S., Mereddy, D., Coursey, A., et al. (2026) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102274 · Cited by 14

Non-Invasive Suicide Risk Prediction Through Speech Analysis

Amiriparian, S., Gerczuk, M., Lutz, J., Strube, W., Papazova, I., Hasan, A., Kathan, A., & Schuller, B. W. (2024) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb64556.2024.10805581 · Cited by 10

Assessing reading fluency in elementary grades: A machine learning approach

Silva, G., Rodrigues, R. L., Amorim, A. N., Jeon, L., Albuquerque, E. X. S., Silva, V. C., da Silva, V. F., Pinheiro, A. L. A., Nunes, J. P. J. R., Souza, S., et al. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100411 · Cited by 10

The spectre of growth in urban transformations: Insights from two Doughnut-oriented municipalities on the negotiation of local development pathways

Schmid, B. (2024) · Urban Studies · doi:10.1177/00420980241305322 · Cited by 9

High-Quality Classroom Dialogue Automatic Analysis System

Jia, L., Sun, H., Jiang, J., & Yang, X. (2025) · Applied Sciences · doi:10.3390/app15031613 · Cited by 9

Towards Multimodal Large-Language Models for Parent-Child Interaction: A Focus on Joint Attention

Shi, W., Le, H. V., & Choo, K. T. W. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720215 · Cited by 9

Live: Learning Video LLM with Streaming Speech Transcription at Scale

Chen, J., Zeng, Z., Lin, Y., Li, W., Ma, Z., & Shou, M. Z. (2025) · 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/cvpr52734.2025.02708 · Cited by 13

Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection

Lian, J., Feng, C., Farooqi, N., Li, S., Kashyap, A., Cho, C. J., Wu, P., Netzorg, R., Li, T., & Anumanchipalli, G. K. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389771 · Cited by 9

FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild

Liu, Z.-S., Courant, R., & Kalogeiton, V. S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02000-2 · Cited by 13

Emilia: A Large-Scale, Extensive, Multilingual, and Diverse Dataset for Speech Generation

He, H., Shang, Z., Wang, C., Li, X., Gu, Y., Hua, H., Liu, L., Yang, C., Li, J., Shi, P., et al. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3612835 · Cited by 8

Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations

Zada, Z., Nastase, S. A., Speer, S. P. H., Mwilambwe-Tshilobo, L., Tsoi, L., Burns, S. M., Falk, E. B., Hasson, U., & Tamir, D. (2025) · Neuron · doi:10.1016/j.neuron.2025.11.004 · Cited by 14

Transcribing and Aligning Conversational Speech: A Hybrid Pipeline Applied to French Conversations

Yamasaki, H., Louradour, J., Hunter, J., & Prévot, L. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389691 · Cited by 7

Enhancing Language Learners’ Comprehensibility through Automated Analysis of Pause Positions and Syllable Prominence

Coulange, S., Kato, T., Rossato, S., & Masperi, M. (2024) · Languages · doi:10.3390/languages9030078 · Cited by 7

Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling

Korbar, B., Huh, J., & Zisserman, A. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446480 · Cited by 7

Enhancing Whisper’s Accuracy and Speed for Indian Languages through Prompt-Tuning and Tokenization

Tripathi, K., Gothi, R., & Wasnik, P. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887595 · Cited by 7

SpeakEasy: Enhancing Text-to-Speech Interactions for Expressive Content Creation

Brade, S., Anderson, S., Kumar, R., Jin, Z., & Truong, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714263 · Cited by 7

Streaming ASR Encoder for Whisper-to-Speech Online Voice Conversion

Avdeeva, A., Gusev, A., Andzhukaev, T., & Ivanov, A. (2023) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2023.3343342 · Cited by 7

Diarist: Streaming Speech Translation with Speaker Diarization

Yang, M., Kanda, N., Wang, X., Chen, J., Wang, P., Xue, J., Li, J., & Yoshioka, T. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446050 · Cited by 7

MMSummary: Multimodal Summary Generation for Fetal Ultrasound Video

Guo, X., Men, Q., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_63 · Cited by 7

VideoMix: Aggregating How-To Videos for Task-Oriented Learning

Yang, S., Truong, A., Kim, J., & Li, D. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712144 · Cited by 7

Visualizing speech styles in captions for deaf and hard-of-hearing viewers

Ahn, S., Kim, J., Shin, C., & Hong, J.-H. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103386 · Cited by 6

Integrating Pause Information with Word Embeddings in Language Models for Alzheimer’s Disease Detection from Spontaneous Speech

Pu, Y., & Zhang, W.-Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888563 · Cited by 9

Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer’s disease risk-enriched cohorts

Xu, L., Chen, K., Mueller, K. D., Liss, J., & Berisha, V. (2024) · Journal of Alzheimer s Disease · doi:10.1177/13872877241300149 · Cited by 8

Student perceptions of the usefulness of core concepts when reasoning in physiology

Cole, E. J., & Doherty, J. H. (2024) · AJP Advances in Physiology Education · doi:10.1152/advan.00198.2024 · Cited by 6

Quantitative analysis of communication dynamics in agile software teams through multimodal analytics

Miranda, D., Noël, R., Godoy, J., Escobedo, C., Cechinel, C., & Muñoz, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-91328-x · Cited by 6

Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability

Parola, A., Jessen, E. T., Rybner, A. S., Mortensen, M. D., Larsen, S. N., Simonsen, A., Lin, J. M., Zhou, Y., Wang, H., Koelkebeck, K., et al. (2025) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf124 · Cited by 6

Scaling Multilingual Visual Speech Recognition

Prajwal, K. R., Hegde, S. B., & Zisserman, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890395 · Cited by 5

Uncertainty modeling in multimodal speech analysis across the psychosis spectrum

Rohanian, M., Hüppi, R. M., Nooralahzadeh, F., Dannecker, N., Pauli, Y., Surbeck, W., Sommer, I. E., Hinzen, W., Langer, N., Krauthammer, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02309-3 · Cited by 12

From manual to machine

Guo, M., & Han, L. (2024) · Interpreting International Journal of Research and Practice in Interpreting · doi:10.1075/intp.00100.guo · Cited by 5

Attentional Engagement with Target and Distractor Streams Predicts Speech Comprehension in Multitalker Environments

Barchet, A. V., Bruera, A., Wend, J., Rimmele, J. M., Obleser, J., & Hartwigsen, G. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0657-25.2025 · Cited by 5

Geometry of neural dynamics along the cortical attractor landscape reflects changes in attention

Song, H., Chen, R.-Q., Botch, T. L., Braver, T. S., Rosenberg, M. D., Zacks, J. M., & Ching, S.-N. (2025) · bioRxiv · doi:10.1038/s41467-026-69041-8 · Cited by 4

Narrative “twist” shifts within-individual neural representations of dissociable story features

Sava‐Segal, C., Grall, C., & Finn, E. S. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2512071123 · Cited by 6

Welcome to the Jungle: Using ARTV to Balance Media Immersion and Reality Awareness for Passengers on Public Transport

Christie, I., McGill, M., & Brewster, S. A. (2024) · ACM International Conference on Interactive Media Experiences · doi:10.1145/3639701.3656321 · Cited by 5

Synthetic AI Data Pipeline for Domain-Specific Speech-to-Text Solutions

Karl, A. L., Fernandes, G. S., Pires, L. A., Serpa, Y. R., & Caminha, C. (2024) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2024.245336 · Cited by 5

Automatic Detection of Articulatory-Based Disfluencies in Primary Progressive Aphasia

Lian, J., Zhou, X., Guo, C., Ye, Z., Ezzes, Z., Vonk, J. M. J., Morin, B. T., Baquirin, D. P., Miller, Z., Tempini, M. L. G., et al. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3579972 · Cited by 5

Hippocampus serves as a repository for spoken and heard word meanings during conversationsPreprint

Chavez, A. G., Yan, X., Franch, M. C., Mickiewicz, E. A., Baltazar, W., Belanger, J. L., Devara, D., Etta, M., Hamre, T., Ismail, T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.20.677504 · Cited by 5

DISCOVER: a Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of human behavior

Hallmen, T., Schiller, D., Vehlen, A., Eberhardt, S. T., Baur, T., Don, D. S. W., Lutz, W., & André, E. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1638539 · Cited by 15

Using Open-Source Automatic Speech Recognition Tools for the Annotation of Dutch Infant-Directed Speech

van der Klis, A., Adriaans, F., Han, M., & Kager, R. (2023) · Multimodal Technologies and Interaction · doi:10.3390/mti7070068 · Cited by 4

Multi-sentence Grounding for Long-Term Instructional Video

Li, Z., Chen, Q., Han, T., Zhang, Y., Wang, Y., & Xie, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72992-8_12 · Cited by 12

EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues

Girmaji, R., Beri, B., Subramanian, R., & Gandhi, V. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712113 · Cited by 6

WhisBERT: Multimodal Text-Audio Language Modeling on 100M Words

Wolf, L., Kotar, K., Tuckute, G., Hosseini, E. A., Regev, T. I., Wilcox, E., & Warstadt, A. S. (2023) · BabyLM Challenge · doi:10.18653/v1/2023.conll-babylm.21 · Cited by 3

AiModerator: A Co-Pilot for Hyper-Contextualization in Political Debate Video

Andrews, P., Borch, N. T., & Fjeld, M. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712148 · Cited by 3

PlantPal: Leveraging Precision Agriculture Robots to Facilitate Remote Engagement in Urban Gardening

Zeqiri, A., Britten, J., Schramm, C., Jansen, P., Rietzler, M., & Rukzio, E. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713180 · Cited by 4

Identifying storytelling in job interviews using deep learning

Germanier, E., He, M., Rufai, A. M., Garner, P. N., Bangerter, A., Renier, L. A., Mast, M. S., & Orji, K. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100688 · Cited by 4

Predicting accentedness and comprehensibility through ASR scores and acoustic features

Dong, W., Catia Cucchiarini, A., van Hout, R. W. N. M., & Strik, H. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101858 · Cited by 3

Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs

Ventura, L., Yang, A., Schmid, C., & Varol, G. (2025) · 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/cvpr52734.2025.01765 · Cited by 3

Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper

Syed, J., Higgs, I. M., Cífka, O., & Sandler, M. B. (2025) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew68306.2025.11152264 · Cited by 3

“As Someone Who is Disabled, I am so thankful for Sex Work”: Alternative Approaches to Access Among Disabled Sex-Workers

Rodolitz, J., Hamilton, V., Tabassum, M., Lerner, A., & Hofmann, M. K. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746375 · Cited by 3

PIA: Deepfake Detection Using Phoneme-Temporal and Identity-Dynamic Analysis

Datta, S. K., Ranga, T., Sun, C., & Lyu, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00170 · Cited by 4

Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers

Yuan, Z., Stefan Benus, A., & D’Ausilio, A. (2024) · Speech prosody · doi:10.21437/speechprosody.2024-255 · Cited by 3

Automatic transcription system for parliamentary debates in the context of assembly of the republic of Portugal

Nascimento, P., Ferreira, J. C., & Batista, F. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10126-4 · Cited by 3

Unpacking the Complexity: Why Current Feedback Systems Fail to Improve Learner Self-Regulation of Participation in Collaborative Activities

Ochôa, X., Huang, X., & Charlton, A. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8355 · Cited by 3

Secondary Teachers’ Self-perceived AI Competences in Relation to Renowned European Digital Competence Frameworks

Jemetz, M., Dolezal, D., & Motschnig, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73474-8_1 · Cited by 5

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

ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription

Le, K., Ho, T. V., Tran, D. K., & Chau, D. T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888640 · Cited by 3

Automating sedation state assessments using natural language processing

Conway, A., Li, J., Rad, M. G., Warren, B. E., Mafeld, S. C., & Taati, B. (2024) · Journal of Nursing Scholarship · doi:10.1111/jnu.12968 · Cited by 3

Computer-Assisted English Presentation Learning System Based on Word Stress in Pronunciation

Tagami, Y., Kojima, T., & Yamaguchi, S. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00271 · Cited by 3

Average Token Delay: A Duration-aware Latency Metric for Simultaneous Translation

Kano, Y., Sudoh, K., & Nakamura, S. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1049 · Cited by 3

Enhancing Automatic Speech Assessment Leveraging Heterogeneous Features and Soft Labels For Ordinal Classification

Peng, W.-H., Chen, S., & Chen, B. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832269 · Cited by 3

Low-Resource Text-to-Speech Synthesis Using Noise-Augmented Training of ForwardTacotron

Lakshminarayana, K. K., Zalkow, F., Dittmar, C., Pia, N., & Habets, E. A. P. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890686 · Cited by 3

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

LiveGuard: Voice Liveness Detection via Wavelet Scattering Transform and Mel Spectrogram Scaling

Shan, L., Zhang, X., Hossen, M. I., & Hei, X. (2025) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn64029.2025.00041 · Cited by 3

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

Context-Aware Interleaved Batching for WhisperXPreprint

Bain, C., & Bain, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.31170

CrisperWhisper: Accurate Timestamps on Verbatim Speech TranscriptionsPreprint

Wagner, L., Thallinger, B., & Zusag, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.16589 · Cited by 3

Turning Whisper into Real-Time Transcription SystemPreprint

Macháček, D., Dabre, R., & Bojar, O. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.14743 · Cited by 7

LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech

Yang, F., Ni, X., Yang, R., Geng, J., Li, Q., Lyu, C., Du, Y., Wang, L., Luo, W., & Zhang, K. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461821

Whispy: Adapting STT Whisper Models to Real-Time EnvironmentsPreprint

Bevilacqua, A., Saviano, P., Amirante, A., & Romano, S. P. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.03484 · Cited by 2