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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/2409.05601">arXiv:2409.05601</a> <span> [<a href="https://arxiv.org/pdf/2409.05601">pdf</a>, <a href="https://arxiv.org/format/2409.05601">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Koluguri%2C+N+R">Nithin Rao Koluguri</a>, <a href="/search/eess?searchtype=author&query=Bartley%2C+T">Travis Bartley</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+H">Hainan Xu</a>, <a href="/search/eess?searchtype=author&query=Hrinchuk%2C+O">Oleksii Hrinchuk</a>, <a href="/search/eess?searchtype=author&query=Balam%2C+J">Jagadeesh Balam</a>, <a href="/search/eess?searchtype=author&query=Ginsburg%2C+B">Boris Ginsburg</a>, <a href="/search/eess?searchtype=author&query=Kucsko%2C+G">Georg Kucsko</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="2409.05601v1-abstract-short" style="display: inline;"> This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on short segments containing only lowercase or partial punctuation and capitalization (PnC) sentences, we propose training on longer utterances that include complete sentences with proper punctuation and capitalization. We ac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05601v1-abstract-full').style.display = 'inline'; document.getElementById('2409.05601v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.05601v1-abstract-full" style="display: none;"> This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on short segments containing only lowercase or partial punctuation and capitalization (PnC) sentences, we propose training on longer utterances that include complete sentences with proper punctuation and capitalization. We achieve this by using the FastConformer architecture which allows training 1 Billion parameter models with sequences up to 60 seconds long with full attention. However, while training with PnC enhances the overall performance, we observed that accuracy plateaus when training on sequences longer than 40 seconds across various evaluation settings. Our proposed method significantly improves punctuation and capitalization accuracy, showing a 25% relative word error rate (WER) improvement on the Earnings-21 and Earnings-22 benchmarks. Additionally, training on longer audio segments increases the overall model accuracy across speech recognition and translation benchmarks. The model weights and training code are open-sourced though NVIDIA NeMo. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.05601v1-abstract-full').style.display = 'none'; document.getElementById('2409.05601v1-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> 9 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">Accepted at SLT 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/2104.02014">arXiv:2104.02014</a> <span> [<a href="https://arxiv.org/pdf/2104.02014">pdf</a>, <a href="https://arxiv.org/format/2104.02014">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="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=O%27Neill%2C+P+K">Patrick K. O'Neill</a>, <a href="/search/eess?searchtype=author&query=Lavrukhin%2C+V">Vitaly Lavrukhin</a>, <a href="/search/eess?searchtype=author&query=Majumdar%2C+S">Somshubra Majumdar</a>, <a href="/search/eess?searchtype=author&query=Noroozi%2C+V">Vahid Noroozi</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yuekai Zhang</a>, <a href="/search/eess?searchtype=author&query=Kuchaiev%2C+O">Oleksii Kuchaiev</a>, <a href="/search/eess?searchtype=author&query=Balam%2C+J">Jagadeesh Balam</a>, <a href="/search/eess?searchtype=author&query=Dovzhenko%2C+Y">Yuliya Dovzhenko</a>, <a href="/search/eess?searchtype=author&query=Freyberg%2C+K">Keenan Freyberg</a>, <a href="/search/eess?searchtype=author&query=Shulman%2C+M+D">Michael D. Shulman</a>, <a href="/search/eess?searchtype=author&query=Ginsburg%2C+B">Boris Ginsburg</a>, <a href="/search/eess?searchtype=author&query=Watanabe%2C+S">Shinji Watanabe</a>, <a href="/search/eess?searchtype=author&query=Kucsko%2C+G">Georg Kucsko</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="2104.02014v2-abstract-short" style="display: inline;"> In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models. This adds complexity and limits performance, as many formatting tasks benefit from semantic information present… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02014v2-abstract-full').style.display = 'inline'; document.getElementById('2104.02014v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2104.02014v2-abstract-full" style="display: none;"> In the English speech-to-text (STT) machine learning task, acoustic models are conventionally trained on uncased Latin characters, and any necessary orthography (such as capitalization, punctuation, and denormalization of non-standard words) is imputed by separate post-processing models. This adds complexity and limits performance, as many formatting tasks benefit from semantic information present in the acoustic signal but absent in transcription. Here we propose a new STT task: end-to-end neural transcription with fully formatted text for target labels. We present baseline Conformer-based models trained on a corpus of 5,000 hours of professionally transcribed earnings calls, achieving a CER of 1.7. As a contribution to the STT research community, we release the corpus free for non-commercial use at https://datasets.kensho.com/datasets/scribe. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2104.02014v2-abstract-full').style.display = 'none'; document.getElementById('2104.02014v2-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> 6 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 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">5 pages, 1 figure. Submitted to INTERSPEECH 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04290">arXiv:2005.04290</a> <span> [<a href="https://arxiv.org/pdf/2005.04290">pdf</a>, <a href="https://arxiv.org/format/2005.04290">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+J">Jocelyn Huang</a>, <a href="/search/eess?searchtype=author&query=Kuchaiev%2C+O">Oleksii Kuchaiev</a>, <a href="/search/eess?searchtype=author&query=O%27Neill%2C+P">Patrick O'Neill</a>, <a href="/search/eess?searchtype=author&query=Lavrukhin%2C+V">Vitaly Lavrukhin</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jason Li</a>, <a href="/search/eess?searchtype=author&query=Flores%2C+A">Adriana Flores</a>, <a href="/search/eess?searchtype=author&query=Kucsko%2C+G">Georg Kucsko</a>, <a href="/search/eess?searchtype=author&query=Ginsburg%2C+B">Boris Ginsburg</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="2005.04290v1-abstract-short" style="display: inline;"> In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks. We start with a pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (German, Spanish and Russian) and (3) application-specific domains. Our experimen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04290v1-abstract-full').style.display = 'inline'; document.getElementById('2005.04290v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04290v1-abstract-full" style="display: none;"> In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks. We start with a pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (German, Spanish and Russian) and (3) application-specific domains. Our experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. It is preferred to fine-tune large models than small pre-trained models, even if the dataset for fine-tuning is small. Moreover, transfer learning significantly speeds up convergence for both very small and very large target datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04290v1-abstract-full').style.display = 'none'; document.getElementById('2005.04290v1-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> 8 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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"> <div class="column"> <ul class="nav-spaced"> <li><a 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