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is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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"> Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sekkat%2C+C">Chlo茅 Sekkat</a>, <a href="/search/cs?searchtype=author&query=Leroy%2C+F">Fanny Leroy</a>, <a href="/search/cs?searchtype=author&query=Mdhaffar%2C+S">Salima Mdhaffar</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+B+P">Blake Perry Smith</a>, <a href="/search/cs?searchtype=author&query=Est%C3%A8ve%2C+Y">Yannick Est猫ve</a>, <a href="/search/cs?searchtype=author&query=Dureau%2C+J">Joseph Dureau</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</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="2405.19342v1-abstract-short" style="display: inline;"> Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce. This is mainly due to the rarity of large datasets with controlled demographic tags. This paper introduces the Sonos Voice Control Bias Assessment Dataset, an open dataset composed of voice assistant requests for North American Engl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19342v1-abstract-full').style.display = 'inline'; document.getElementById('2405.19342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.19342v1-abstract-full" style="display: none;"> Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce. This is mainly due to the rarity of large datasets with controlled demographic tags. This paper introduces the Sonos Voice Control Bias Assessment Dataset, an open dataset composed of voice assistant requests for North American English in the music domain (1,038 speakers, 166 hours, 170k audio samples, with 9,040 unique labelled transcripts) with a controlled demographic diversity (gender, age, dialectal region and ethnicity). We also release a statistical demographic bias assessment methodology, at the univariate and multivariate levels, tailored to this specific use case and leveraging spoken language understanding metrics rather than transcription accuracy, which we believe is a better proxy for user experience. To demonstrate the capabilities of this dataset and statistical method to detect demographic bias, we consider a pair of state-of-the-art Automatic Speech Recognition and Spoken Language Understanding models. Results show statistically significant differences in performance across age, dialectal region and ethnicity. Multivariate tests are crucial to shed light on mixed effects between dialectal region, gender and age. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19342v1-abstract-full').style.display = 'none'; document.getElementById('2405.19342v1-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> 14 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.02143">arXiv:2011.02143</a> <span> [<a href="https://arxiv.org/pdf/2011.02143">pdf</a>, <a href="https://arxiv.org/format/2011.02143">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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=d%27Ascoli%2C+S">St茅phane d'Ascoli</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Caltagirone%2C+F">Francesco Caltagirone</a>, <a href="/search/cs?searchtype=author&query=Caulier%2C+A">Alexandre Caulier</a>, <a href="/search/cs?searchtype=author&query=Lelarge%2C+M">Marc Lelarge</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="2011.02143v1-abstract-short" style="display: inline;"> Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. Our contribution is twofold. First we show how to optimally train and contr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.02143v1-abstract-full').style.display = 'inline'; document.getElementById('2011.02143v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.02143v1-abstract-full" style="display: none;"> Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. Our contribution is twofold. First we show how to optimally train and control the generation of intent-specific sentences using a conditional variational autoencoder. Then we introduce a new protocol called query transfer that allows to leverage a large unlabelled dataset, possibly containing irrelevant queries, to extract relevant information. Comparison with two different baselines shows that this method, in the appropriate regime, consistently improves the diversity of the generated queries without compromising their quality. We also demonstrate the effectiveness of our generation method as a data augmentation technique for language modelling tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.02143v1-abstract-full').style.display = 'none'; document.getElementById('2011.02143v1-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> 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </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">arXiv admin note: substantial text overlap with arXiv:1911.03698</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.01709">arXiv:2011.01709</a> <span> [<a href="https://arxiv.org/pdf/2011.01709">pdf</a>, <a href="https://arxiv.org/format/2011.01709">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey 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="Machine Learning">cs.LG</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"> Small footprint Text-Independent Speaker Verification for Embedded Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Balian%2C+J">Julien Balian</a>, <a href="/search/cs?searchtype=author&query=Tavarone%2C+R">Raffaele Tavarone</a>, <a href="/search/cs?searchtype=author&query=Poumeyrol%2C+M">Mathieu Poumeyrol</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</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="2011.01709v2-abstract-short" style="display: inline;"> Deep neural network approaches to speaker verification have proven successful, but typical computational requirements of State-Of-The-Art (SOTA) systems make them unsuited for embedded applications. In this work, we present a two-stage model architecture orders of magnitude smaller than common solutions (237.5K learning parameters, 11.5MFLOPS) reaching a competitive result of 3.31% Equal Error Rat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01709v2-abstract-full').style.display = 'inline'; document.getElementById('2011.01709v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.01709v2-abstract-full" style="display: none;"> Deep neural network approaches to speaker verification have proven successful, but typical computational requirements of State-Of-The-Art (SOTA) systems make them unsuited for embedded applications. In this work, we present a two-stage model architecture orders of magnitude smaller than common solutions (237.5K learning parameters, 11.5MFLOPS) reaching a competitive result of 3.31% Equal Error Rate (EER) on the well established VoxCeleb1 verification test set. We demonstrate the possibility of running our solution on small devices typical of IoT systems such as the Raspberry Pi 3B with a latency smaller than 200ms on a 5s long utterance. Additionally, we evaluate our model on the acoustically challenging VOiCES corpus. We report a limited increase in EER of 2.6 percentage points with respect to the best scoring model of the 2019 VOiCES from a Distance Challenge, against a reduction of 25.6 times in the number of learning parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.01709v2-abstract-full').style.display = 'none'; document.getElementById('2011.01709v2-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> 21 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Acoustics, Speech and Signal Processing (ICASSP), 2021 IEEE International Conference </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.03698">arXiv:1911.03698</a> <span> [<a href="https://arxiv.org/pdf/1911.03698">pdf</a>, <a href="https://arxiv.org/format/1911.03698">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Conditioned Query Generation for Task-Oriented Dialogue Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=d%27Ascoli%2C+S">St茅phane d'Ascoli</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Caltagirone%2C+F">Francesco Caltagirone</a>, <a href="/search/cs?searchtype=author&query=Caulier%2C+A">Alexandre Caulier</a>, <a href="/search/cs?searchtype=author&query=Lelarge%2C+M">Marc Lelarge</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="1911.03698v1-abstract-short" style="display: inline;"> Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. In this paper we propose a novel controlled data generation method that cou… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.03698v1-abstract-full').style.display = 'inline'; document.getElementById('1911.03698v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.03698v1-abstract-full" style="display: none;"> Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. In this paper we propose a novel controlled data generation method that could be used as a training augmentation framework for closed-domain dialogue. Our contribution is twofold. First we show how to optimally train and control the generation of intent-specific sentences using a conditional variational autoencoder. Then we introduce a novel protocol called query transfer that allows to leverage a broad, unlabelled dataset to extract relevant information. Comparison with two different baselines shows that our method, in the appropriate regime, consistently improves the diversity of the generated queries without compromising their quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.03698v1-abstract-full').style.display = 'none'; document.getElementById('1911.03698v1-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 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1811.07684">arXiv:1811.07684</a> <span> [<a href="https://arxiv.org/pdf/1811.07684">pdf</a>, <a href="https://arxiv.org/format/1811.07684">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey 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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Efficient keyword spotting using dilated convolutions and gating </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Chlieh%2C+M">Mohammed Chlieh</a>, <a href="/search/cs?searchtype=author&query=Gisselbrecht%2C+T">Thibault Gisselbrecht</a>, <a href="/search/cs?searchtype=author&query=Leroy%2C+D">David Leroy</a>, <a href="/search/cs?searchtype=author&query=Poumeyrol%2C+M">Mathieu Poumeyrol</a>, <a href="/search/cs?searchtype=author&query=Lavril%2C+T">Thibaut Lavril</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="1811.07684v2-abstract-short" style="display: inline;"> We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations. Gated ac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.07684v2-abstract-full').style.display = 'inline'; document.getElementById('1811.07684v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1811.07684v2-abstract-full" style="display: none;"> We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations. Gated activations and residual connections are also added, following a similar configuration to WaveNet. In addition, we apply a custom target labeling that back-propagates loss from specific frames of interest, therefore yielding higher accuracy and only requiring to detect the end of the keyword. Our experimental results show that our model outperforms a max-pooling loss trained recurrent neural network using LSTM cells, with a significant decrease in false rejection rate. The underlying dataset - "Hey Snips" utterances recorded by over 2.2K different speakers - has been made publicly available to establish an open reference for wake-word detection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1811.07684v2-abstract-full').style.display = 'none'; document.getElementById('1811.07684v2-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> 18 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 November, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2018. </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 for publication to ICASSP 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.12735">arXiv:1810.12735</a> <span> [<a href="https://arxiv.org/pdf/1810.12735">pdf</a>, <a href="https://arxiv.org/ps/1810.12735">ps</a>, <a href="https://arxiv.org/format/1810.12735">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="Machine Learning">cs.LG</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"> Spoken Language Understanding on the Edge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Saade%2C+A">Alaa Saade</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Caulier%2C+A">Alexandre Caulier</a>, <a href="/search/cs?searchtype=author&query=Dureau%2C+J">Joseph Dureau</a>, <a href="/search/cs?searchtype=author&query=Ball%2C+A">Adrien Ball</a>, <a href="/search/cs?searchtype=author&query=Bluche%2C+T">Th茅odore Bluche</a>, <a href="/search/cs?searchtype=author&query=Leroy%2C+D">David Leroy</a>, <a href="/search/cs?searchtype=author&query=Doumouro%2C+C">Cl茅ment Doumouro</a>, <a href="/search/cs?searchtype=author&query=Gisselbrecht%2C+T">Thibault Gisselbrecht</a>, <a href="/search/cs?searchtype=author&query=Caltagirone%2C+F">Francesco Caltagirone</a>, <a href="/search/cs?searchtype=author&query=Lavril%2C+T">Thibaut Lavril</a>, <a href="/search/cs?searchtype=author&query=Primet%2C+M">Ma毛l Primet</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="1810.12735v2-abstract-short" style="display: inline;"> We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.12735v2-abstract-full').style.display = 'inline'; document.getElementById('1810.12735v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.12735v2-abstract-full" style="display: none;"> We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and in the hope that they can prove useful to the SLU community. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.12735v2-abstract-full').style.display = 'none'; document.getElementById('1810.12735v2-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 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </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">arXiv admin note: text overlap with arXiv:1805.10190</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1810.05512">arXiv:1810.05512</a> <span> [<a href="https://arxiv.org/pdf/1810.05512">pdf</a>, <a href="https://arxiv.org/format/1810.05512">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Federated Learning for Keyword Spotting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Leroy%2C+D">David Leroy</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Lavril%2C+T">Thibaut Lavril</a>, <a href="/search/cs?searchtype=author&query=Gisselbrecht%2C+T">Thibault Gisselbrecht</a>, <a href="/search/cs?searchtype=author&query=Dureau%2C+J">Joseph Dureau</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="1810.05512v4-abstract-short" style="display: inline;"> We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the "Hey Snips" wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.05512v4-abstract-full').style.display = 'inline'; document.getElementById('1810.05512v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1810.05512v4-abstract-full" style="display: none;"> We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the "Hey Snips" wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is a reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is being open sourced with the aim of fostering further transparent research in the application of federated learning to speech data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1810.05512v4-abstract-full').style.display = 'none'; document.getElementById('1810.05512v4-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> 18 February, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 October, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2018. </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 for publication to ICASSP 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1805.10190">arXiv:1805.10190</a> <span> [<a href="https://arxiv.org/pdf/1805.10190">pdf</a>, <a href="https://arxiv.org/format/1805.10190">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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Saade%2C+A">Alaa Saade</a>, <a href="/search/cs?searchtype=author&query=Ball%2C+A">Adrien Ball</a>, <a href="/search/cs?searchtype=author&query=Bluche%2C+T">Th茅odore Bluche</a>, <a href="/search/cs?searchtype=author&query=Caulier%2C+A">Alexandre Caulier</a>, <a href="/search/cs?searchtype=author&query=Leroy%2C+D">David Leroy</a>, <a href="/search/cs?searchtype=author&query=Doumouro%2C+C">Cl茅ment Doumouro</a>, <a href="/search/cs?searchtype=author&query=Gisselbrecht%2C+T">Thibault Gisselbrecht</a>, <a href="/search/cs?searchtype=author&query=Caltagirone%2C+F">Francesco Caltagirone</a>, <a href="/search/cs?searchtype=author&query=Lavril%2C+T">Thibaut Lavril</a>, <a href="/search/cs?searchtype=author&query=Primet%2C+M">Ma毛l Primet</a>, <a href="/search/cs?searchtype=author&query=Dureau%2C+J">Joseph Dureau</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="1805.10190v3-abstract-short" style="display: inline;"> This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.10190v3-abstract-full').style.display = 'inline'; document.getElementById('1805.10190v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1805.10190v3-abstract-full" style="display: none;"> This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1805.10190v3-abstract-full').style.display = 'none'; document.getElementById('1805.10190v3-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 December, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 May, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2018. </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">29 pages, 9 figures, 17 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1803.09533">arXiv:1803.09533</a> <span> [<a href="https://arxiv.org/pdf/1803.09533">pdf</a>, <a href="https://arxiv.org/format/1803.09533">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Deep Representation for Patient Visits from Electronic Health Records </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Escudi%C3%A9%2C+J">Jean-Baptiste Escudi茅</a>, <a href="/search/cs?searchtype=author&query=Saade%2C+A">Alaa Saade</a>, <a href="/search/cs?searchtype=author&query=Coucke%2C+A">Alice Coucke</a>, <a href="/search/cs?searchtype=author&query=Lelarge%2C+M">Marc Lelarge</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="1803.09533v1-abstract-short" style="display: inline;"> We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models anticipated to drive personalized medicine and improve healthcare quality. Thes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09533v1-abstract-full').style.display = 'inline'; document.getElementById('1803.09533v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1803.09533v1-abstract-full" style="display: none;"> We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models anticipated to drive personalized medicine and improve healthcare quality. These embeddings are learned using a deep neural network trained to predict ICD diagnosis categories. We show that our embeddings capture relevant clinical informations and can be used directly as input to standard machine learning algorithms like multi-output classifiers for ICD code prediction. We also show that important medical informations correspond to particular directions in our embedding space. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1803.09533v1-abstract-full').style.display = 'none'; document.getElementById('1803.09533v1-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 March, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2018. </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <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"> <!-- MetaColumn 1 --> <div class="column"> <div class="columns"> <div class="column"> <ul 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