How Might We Create Better Benchmarks for Speech Recognition?

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SWITCHBOARD: telephone speech corpus for research and development

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The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions

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The intersection of sex and social class in the course of linguistic change

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The design for the wall street journal-based CSR corpus

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Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

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The AMI Meeting Corpus: A Pre-announcement

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Common Voice: A Massively-Multilingual Speech Corpus

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Racial disparities in automated speech recognition

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A Handbook of Varieties of English

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The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text

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English Conversational Telephone Speech Recognition by Humans and Machines

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Normalization of non-standard words

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Gender and Dialect Bias in YouTube’s Automatic Captions

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Speech recognition

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Toward Human Parity in Conversational Speech Recognition

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Pushing the Limits of Semi-Supervised Learning for Automatic Speech RecognitionPreprint

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A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency

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Effects of speaking rate and word frequency on pronunciations in convertional speech

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What size test set gives good error rate estimates?

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Is word error rate a good indicator for spoken language understanding accuracy

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The Third DIHARD Diarization Challenge

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Characterising Bias in Compressed ModelsPreprint

Hooker, S., Moorosi, N., Clark, G., Samy Bengio, A., & Denton, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03058 · Cited by 132 · see citing works

Automatic Recognition of Spontaneous Speech for Access to Multilingual Oral History Archives

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What Do Compressed Deep Neural Networks Forget?Preprint

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The design for the wall street journal-based CSR corpus

Paul, D. B., & Baker, J. M. (1992) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1992-277 · Cited by 121 · see citing works

Personalizing ASR for Dysarthric and Accented Speech with Limited Data

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FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN

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What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness

Andrus, M., Spitzer, E., Brown, J., & Xiang, A. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445888 · Cited by 111 · see citing works

Towards Fast and Accurate Streaming End-To-End ASR

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Het Corpus Gesproken Nederlands

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Improving Noise Robust Automatic Speech Recognition with Single-Channel Time-Domain Enhancement Network

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Large vocabulary automatic speech recognition for children

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Bringing contextual information to google speech recognition

Aleksic, P. S., Ghodsi, M., Michaely, A. H., Allauzen, C., Hall, K., Brian Roark, A., Rybach, D., & Moreno, P. J. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-177 · Cited by 91 · see citing works

On the effects of speech rate in large vocabulary speech recognition systems

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Effects of Talker Dialect, Gender & Race on Accuracy of Bing Speech and YouTube Automatic Captions

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Diversity and Inclusion Metrics in Subset Selection

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Assessing the accuracy of automatic speech recognition for psychotherapy

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Towards Measuring Fairness in AI: The Casual Conversations Dataset

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Speech Recognition for Medical Conversations

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100,000 Podcasts: A Spoken English Document Corpus

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FastEmit: Low-Latency Streaming ASR with Sequence-Level Emission Regularization

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Calibration of Confidence Measures in Speech Recognition

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Quantifying Bias in Automatic Speech RecognitionPreprint

Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15122 · Cited by 61 · see citing works

ASR-based dictation practice for second language pronunciation improvement

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Rethinking Evaluation in ASR: Are Our Models Robust Enough?

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Designing an Online Infrastructure for Collecting AI Data From People With Disabilities

Park, J. S., Bragg, D., Kamar, E., & Morris, M. R. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445870 · Cited by 50 · see citing works

Cross Domain Automatic Transcription on the TC-STAR EPPS Corpus

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Speech Databases of Typical Children and Children with SLI

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Structure of pauses in speech in the context of speaker verification and classification of speech type

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WER we are and WER we think we are

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Forgotten Little Words: How Backchannels and Particles May Facilitate Speech Planning in Conversation?

Knudsen, B., Creemers, A., & Meyer, A. S. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.593671 · Cited by 38 · see citing works

Improving Performance of End-to-End ASR on Numeric Sequences

Peyser, C., Zhang, H., Sainath, T. N., & Wu, Z. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1345 · Cited by 37 · see citing works

SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted End-to-End Speech Recognition

O’Neill, P. K., Lavrukhin, V., Majumdar, S., Noroozi, V., Zhang, Y., Kuchaiev, O., Balam, J., Dovzhenko, Y., Freyberg, K., Shulman, M. D., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1860 · Cited by 36 · see citing works

MLPerf Training Benchmark

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Towards Understanding ASR Error Correction for Medical Conversations

Mani, A., Palaskar, S., & Konam, S. (2020) · doi:10.18653/v1/2020.nlpmc-1.2 · Cited by 32 · see citing works

Effectively Building Tera Scale MaxEnt Language Models Incorporating Non-Linguistic Signals

Biadsy, F., Ghodsi, M., & Caseiro, D. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1203 · Cited by 30 · see citing works

Automatic speech recognition performance on a voicemail transcription task

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Emitting Word Timings with End-to-End Models

Sainath, T. N., Pang, R., Rybach, D., García, B., & Strohman, T. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1059 · Cited by 27 · see citing works

Dissecting User-Perceived Latency of On-Device E2E Speech Recognition

Shangguan, Y., Prabhavalkar, R., Su, H., Mahadeokar, J., Shi, Y., Zhou, J., Wu, C., Le, D., Kalinli, O., Fuegen, C., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1887 · Cited by 27 · see citing works

Sources of variation in the speech of African Americans: Perspectives from sociophonetics

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Shrinking Bigfoot: Reducing wav2vec 2.0 footprint

Peng, Z., Budhkar, A., Tuil, I., Levy, J., Sobhani, P., Cohen, R., & Nassour, J. (2021) · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP) · doi:10.18653/v1/2021.sustainlp-1.14 · Cited by 22 · see citing works

End-to-End Speech Recognition and Disfluency Removal

Lou, P. J., & Johnson, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.186 · Cited by 20 · see citing works

Improved Robustness to Disfluencies in Rnn-Transducer Based Speech Recognition

Mendelev, V., Raissi, T., Camporese, G., & Giollo, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413618 · Cited by 19 · see citing works

Comparative analysis and review of interactive voice response systems

Inam, I. A., Azeta, A. A., & Daramola, O. J. (2017) · Conference on Information Communications Technology and Society (ICTAS) · doi:10.1109/ictas.2017.7920660 · Cited by 17 · see citing works

Learning Personalized Pronunciations for Contact Name Recognition

Bruguier, A., Peng, F., & Beaufays, F. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-537 · Cited by 14 · see citing works

Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer

Shangguan, Y., Knister, K., He, Y., Mcgraw, I. C., & Beaufays, F. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1194 · Cited by 11 · see citing works

Effect of different sampling rates and feature vector sizes on speech recognition performance

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El corpus ESLORA de español oral: diseño, desarrollo y explotación

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Speech recognition of different sampling rates using fractal code descriptor

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Decision tree state clustering with word and syllable features

Liao, H., Alberti, C., Bacchiani, M., & Siohan, O. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-28 · Cited by 9 · see citing works

Individual Variation in Language Attitudes Toward Voice-AI: The Role of Listeners’ Autistic-Like Traits

Cohn, M., Sarian, M., Predeck, K., & Zellou, G. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1339 · Cited by 8 · see citing works

IGC-Parl: Icelandic Corpus of Parliamentary Proceedings

Steingrímsson, S., Barkarson, S., & Örnólfsson, G. T. (2020) · Cited by 7 · see citing works

Rethinking the corpus: moving towards dynamic linguistic resources

Rosenberg, A. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-400 · Cited by 6 · see citing works

Initiation, Progression, and Conditioning of the Short-Front Vowel Shift in Australia

Grama, J., Travis, C. E., & Ochoa, S. G. (2019) · ANU Open Research (Australian National University) · Cited by 6 · see citing works

Attentional Speech Recognition Models Misbehave on Out-of-domain UtterancesPreprint

Keung, P., Niu, W., Lu, Y., Salazar, J., & Bhardwaj, V. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05150 · Cited by 5 · see citing works

Algorithmic Exploration of American English Dialects

Aksënova, A., Bruguier, A., Ritchart-Scott, A., & Mendlovic, U. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053751 · Cited by 4 · see citing works

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 · see citing works

A computer-assisted consecutive interpreting workflow: training and evaluation

Chen, S., & Kruger, J. (2024) · The Interpreter and Translator Trainer · doi:10.1080/1750399x.2024.2373553 · Cited by 19 · see citing works

Large Language Model Based Generative Error Correction: A Challenge and Baselines For Speech Recognition, Speaker Tagging, and Emotion Recognition

Yang, C.-H. H., Park, T., Gong, Y., Li, Y., Chen, Z., Lin, Y., Chen, C., Hu, Y., Dhawan, K., Żelasko, P., et al. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832176 · Cited by 11 · see citing works

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 · see citing works

The AI Act in a law enforcement context: The case of automatic speech recognition for transcribing investigative interviews

Stoykova, R., Porter, K., & Beka, T. (2024) · Forensic Science International Synergy · doi:10.1016/j.fsisyn.2024.100563 · Cited by 7 · see citing works

The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

Liesenfeld, A., Lopez, A., & Dingemanse, M. (2023) · SIGDIAL Conferences · doi:10.18653/v1/2023.sigdial-1.45 · Cited by 18 · see citing works

Accuracy of AI-generated Captions With Collaborative Manual Corrections in Real-Time

Kuhn, K., Kersken, V., & Zimmermann, G. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3585724 · Cited by 7 · see citing works

Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation

Hutchinson, B., Louro, C. R., Collard, G. D., & Cooper, N. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732010 · Cited by 4 · see citing works

Sparse summary generation

Zhao, S., He, T., & Wen, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03450-2 · Cited by 4 · see citing works

A Data Perspective on Ethical Challenges in Voice Biometrics Research

Leschanowsky, A., Rusti, C., Quinlan, C., Pnacekova, M., Gorce, L., & Hutiri, W. T. (2024) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2024.3446846 · Cited by 7 · see citing works

Reflections on AI-Augmented Professional Development in Special Education

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Voice Anonymization for All-Bias Evaluation of the Voice Privacy Challenge Baseline Systems

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Large Language Models Meet Speech Recognition

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A survey of Polish ASR speech datasets

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Comparative Analysis of Energy Consumption and Carbon Footprint in Automatic Speech Recognition Systems: A Case Study Comparing Whisper and Google Speech-to-Text

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BIGOS - Benchmark Intended Grouping of Open Speech Corpora for Polish Automatic Speech Recognition

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A new corpus of geolocated ASR transcripts from Germany

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When Worlds Collide

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Automatic Speech Recognition Models for Transcribing Norwegian Investigative Interviews: Compliance with the Draft Ai Act and Fairness StudyPreprint

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Evaluating the impact of different acoustic contexts on German speech recognition

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Integrating Speech Recognition and NLP for Efficient Transcription Solutions

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Investigating benchmarking techniques for unveiling large language model performance

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Sustainable Speech Recognition: Energy, Carbon, and Performance Comparison of Whisper (Base and Large) and Google Speech-to-Text V2 (Chirp/USM)

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'I have to talk proper white ways': Australian Aboriginal English Speakers' Experiences with Voice Technologies

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U.S. Southerners’ Attitudes Towards Algorithmic Analysis of Voice Data for High-Stakes Employment and Education Evaluations

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A Framework for Speech Recognition Benchmarking.

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On the Use of Information Retrieval Measures for Speech Recognition Evaluation

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SpeechColab leaderboard: An open-source platform for automatic speech recognition evaluation

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Beyond Word Error Rate: Auditing the Diversity Tax in Speech Recognition through Dataset CartographyPreprint

Cheng, T.-H., Clemmensen, L. K. H., & Das, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2603.05267

Towards Orthographically-Informed Evaluation of Speech Recognition Systems for Indian Languages

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