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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03930">arXiv:2410.03930</a> <span> [<a href="https://arxiv.org/pdf/2410.03930">pdf</a>, <a href="https://arxiv.org/ps/2410.03930">ps</a>, <a href="https://arxiv.org/format/2410.03930">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Reverb: Open-Source ASR and Diarization from Rev </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhandari%2C+N">Nishchal Bhandari</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+D">Danny Chen</a>, <a href="/search/cs?searchtype=author&query=Fern%C3%A1ndez%2C+M+%C3%81+d+R">Miguel 脕ngel del R铆o Fern谩ndez</a>, <a href="/search/cs?searchtype=author&query=Delworth%2C+N">Natalie Delworth</a>, <a href="/search/cs?searchtype=author&query=Fox%2C+J+D">Jennifer Drexler Fox</a>, <a href="/search/cs?searchtype=author&query=Jett%C3%A9%2C+M">Mig眉el Jett茅</a>, <a href="/search/cs?searchtype=author&query=McNamara%2C+Q">Quinten McNamara</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+C">Corey Miller</a>, <a href="/search/cs?searchtype=author&query=Novotn%C3%BD%2C+O">Ond艡ej Novotn媒</a>, <a href="/search/cs?searchtype=author&query=Profant%2C+J">J谩n Profant</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+N">Nan Qin</a>, <a href="/search/cs?searchtype=author&query=Ratajczak%2C+M">Martin Ratajczak</a>, <a href="/search/cs?searchtype=author&query=Robichaud%2C+J">Jean-Philippe Robichaud</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03930v1-abstract-short" style="display: inline;"> Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these releases will spur research and innovation in the fast-moving domain of voice technology. The speech recognition models released today outperform all exi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03930v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03930v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03930v1-abstract-full" style="display: none;"> Today, we are open-sourcing our core speech recognition and diarization models for non-commercial use. We are releasing both a full production pipeline for developers as well as pared-down research models for experimentation. Rev hopes that these releases will spur research and innovation in the fast-moving domain of voice technology. The speech recognition models released today outperform all existing open source speech recognition models across a variety of long-form speech recognition domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03930v1-abstract-full').style.display = 'none'; document.getElementById('2410.03930v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.03059">arXiv:2409.03059</a> <span> [<a href="https://arxiv.org/pdf/2409.03059">pdf</a>, <a href="https://arxiv.org/format/2409.03059">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.21437/Interspeech.2024-2300">10.21437/Interspeech.2024-2300 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Quantification of stylistic differences in human- and ASR-produced transcripts of African American English </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Heuser%2C+A">Annika Heuser</a>, <a href="/search/cs?searchtype=author&query=Kendall%2C+T">Tyler Kendall</a>, <a href="/search/cs?searchtype=author&query=del+Rio%2C+M">Miguel del Rio</a>, <a href="/search/cs?searchtype=author&query=McNamara%2C+Q">Quinten McNamara</a>, <a href="/search/cs?searchtype=author&query=Bhandari%2C+N">Nishchal Bhandari</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+C">Corey Miller</a>, <a href="/search/cs?searchtype=author&query=Jett%C3%A9%2C+M">Mig眉el Jett茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.03059v1-abstract-short" style="display: inline;"> Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significant role in ASR performance evaluation when differences exist between training and test datasets. The problem is compounded for speech from underrepres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.03059v1-abstract-full').style.display = 'inline'; document.getElementById('2409.03059v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.03059v1-abstract-full" style="display: none;"> Common measures of accuracy used to assess the performance of automatic speech recognition (ASR) systems, as well as human transcribers, conflate multiple sources of error. Stylistic differences, such as verbatim vs non-verbatim, can play a significant role in ASR performance evaluation when differences exist between training and test datasets. The problem is compounded for speech from underrepresented varieties, where the speech to orthography mapping is not as standardized. We categorize the kinds of stylistic differences between 6 transcription versions, 4 human- and 2 ASR-produced, of 10 hours of African American English (AAE) speech. Focusing on verbatim features and AAE morphosyntactic features, we investigate the interactions of these categories with how well transcripts can be compared via word error rate (WER). The results, and overall analysis, help clarify how ASR outputs are a function of the decisions made by the training data's human transcribers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.03059v1-abstract-full').style.display = 'none'; document.getElementById('2409.03059v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Published in Interspeech 2024 Proceedings, 5 pages excluding references, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15013">arXiv:2309.15013</a> <span> [<a href="https://arxiv.org/pdf/2309.15013">pdf</a>, <a href="https://arxiv.org/format/2309.15013">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Updated Corpora and Benchmarks for Long-Form Speech Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fox%2C+J+D">Jennifer Drexler Fox</a>, <a href="/search/cs?searchtype=author&query=Raj%2C+D">Desh Raj</a>, <a href="/search/cs?searchtype=author&query=Delworth%2C+N">Natalie Delworth</a>, <a href="/search/cs?searchtype=author&query=McNamara%2C+Q">Quinn McNamara</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+C">Corey Miller</a>, <a href="/search/cs?searchtype=author&query=Jett%C3%A9%2C+M">Mig眉el Jett茅</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15013v1-abstract-short" style="display: inline;"> The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances. In most real-word ASR use-cases, however, test audio is not segmented, leading to a mismatch between inference-time conditions and models trained on segmented utterances. In this paper, we re-release three standard ASR corpora - TED-LIUM 3, Gigapeech, and VoxPopuli-en -… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15013v1-abstract-full').style.display = 'inline'; document.getElementById('2309.15013v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15013v1-abstract-full" style="display: none;"> The vast majority of ASR research uses corpora in which both the training and test data have been pre-segmented into utterances. In most real-word ASR use-cases, however, test audio is not segmented, leading to a mismatch between inference-time conditions and models trained on segmented utterances. In this paper, we re-release three standard ASR corpora - TED-LIUM 3, Gigapeech, and VoxPopuli-en - with updated transcription and alignments to enable their use for long-form ASR research. We use these reconstituted corpora to study the train-test mismatch problem for transducers and attention-based encoder-decoders (AEDs), confirming that AEDs are more susceptible to this issue. Finally, we benchmark a simple long-form training for these models, showing its efficacy for model robustness under this domain shift. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15013v1-abstract-full').style.display = 'none'; document.getElementById('2309.15013v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to ICASSP 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.11348">arXiv:2104.11348</a> <span> [<a href="https://arxiv.org/pdf/2104.11348">pdf</a>, <a href="https://arxiv.org/format/2104.11348">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.21437/Interspeech.2021-1915">10.21437/Interspeech.2021-1915 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Earnings-21: A Practical Benchmark for ASR in the Wild </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Del+Rio%2C+M">Miguel Del Rio</a>, <a href="/search/cs?searchtype=author&query=Delworth%2C+N">Natalie Delworth</a>, <a href="/search/cs?searchtype=author&query=Westerman%2C+R">Ryan Westerman</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+M">Michelle Huang</a>, <a href="/search/cs?searchtype=author&query=Bhandari%2C+N">Nishchal Bhandari</a>, <a href="/search/cs?searchtype=author&query=Palakapilly%2C+J">Joseph Palakapilly</a>, <a href="/search/cs?searchtype=author&query=McNamara%2C+Q">Quinten McNamara</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+J">Joshua Dong</a>, <a href="/search/cs?searchtype=author&query=Zelasko%2C+P">Piotr Zelasko</a>, <a href="/search/cs?searchtype=author&query=Jette%2C+M">Miguel Jette</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.11348v3-abstract-short" style="display: inline;"> Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR systems in the wild with special a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.11348v3-abstract-full').style.display = 'inline'; document.getElementById('2104.11348v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.11348v3-abstract-full" style="display: none;"> Commonly used speech corpora inadequately challenge academic and commercial ASR systems. In particular, speech corpora lack metadata needed for detailed analysis and WER measurement. In response, we present Earnings-21, a 39-hour corpus of earnings calls containing entity-dense speech from nine different financial sectors. This corpus is intended to benchmark ASR systems in the wild with special attention towards named entity recognition. We benchmark four commercial ASR models, two internal models built with open-source tools, and an open-source LibriSpeech model and discuss their differences in performance on Earnings-21. Using our recently released fstalign tool, we provide a candid analysis of each model's recognition capabilities under different partitions. Our analysis finds that ASR accuracy for certain NER categories is poor, presenting a significant impediment to transcript comprehension and usage. Earnings-21 bridges academic and commercial ASR system evaluation and enables further research on entity modeling and WER on real world audio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.11348v3-abstract-full').style.display = 'none'; document.getElementById('2104.11348v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 22 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to INTERSPEECH 2021. June 15 2021: Addressing the comments of reviewers and updating the results of our internal ESPNet model. The results do not change our conclusions. April 28th, 2021: We found and resolved an issue in our experimental evaluation that scored the LibriSpeech model at ~20% worse relative WER than the actual WER. The updated results do not affect our conclusions</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2104.10747">arXiv:2104.10747</a> <span> [<a href="https://arxiv.org/pdf/2104.10747">pdf</a>, <a href="https://arxiv.org/ps/2104.10747">ps</a>, <a href="https://arxiv.org/format/2104.10747">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Accented Speech Recognition: A Survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hinsvark%2C+A">Arthur Hinsvark</a>, <a href="/search/cs?searchtype=author&query=Delworth%2C+N">Natalie Delworth</a>, <a href="/search/cs?searchtype=author&query=Del+Rio%2C+M">Miguel Del Rio</a>, <a href="/search/cs?searchtype=author&query=McNamara%2C+Q">Quinten McNamara</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+J">Joshua Dong</a>, <a href="/search/cs?searchtype=author&query=Westerman%2C+R">Ryan Westerman</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+M">Michelle Huang</a>, <a href="/search/cs?searchtype=author&query=Palakapilly%2C+J">Joseph Palakapilly</a>, <a href="/search/cs?searchtype=author&query=Drexler%2C+J">Jennifer Drexler</a>, <a href="/search/cs?searchtype=author&query=Pirkin%2C+I">Ilya Pirkin</a>, <a href="/search/cs?searchtype=author&query=Bhandari%2C+N">Nishchal Bhandari</a>, <a href="/search/cs?searchtype=author&query=Jette%2C+M">Miguel Jette</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2104.10747v2-abstract-short" style="display: inline;"> Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.10747v2-abstract-full').style.display = 'inline'; document.getElementById('2104.10747v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.10747v2-abstract-full" style="display: none;"> Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark makes research and comparison especially difficult. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.10747v2-abstract-full').style.display = 'none'; document.getElementById('2104.10747v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2021. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> 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