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type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button 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/2409.16117">arXiv:2409.16117</a> <span> [<a href="https://arxiv.org/pdf/2409.16117">pdf</a>, <a href="https://arxiv.org/ps/2409.16117">ps</a>, <a href="https://arxiv.org/format/2409.16117">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="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ku%2C+P">Pin-Jui Ku</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+A+H">Alexander H. Liu</a>, <a href="/search/cs?searchtype=author&query=Korostik%2C+R">Roman Korostik</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+S">Sung-Feng Huang</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+S">Szu-Wei Fu</a>, <a href="/search/cs?searchtype=author&query=Juki%C4%87%2C+A">Ante Juki膰</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="2409.16117v2-abstract-short" style="display: inline;"> This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coefficients, our model does not rely on any vocoders for time-domain signal reconstruction. As a result, our model simplifies the synthesis process and removes the quality upper-bound introduced by any mel-spectrogram vocoder… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16117v2-abstract-full').style.display = 'inline'; document.getElementById('2409.16117v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16117v2-abstract-full" style="display: none;"> This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coefficients, our model does not rely on any vocoders for time-domain signal reconstruction. As a result, our model simplifies the synthesis process and removes the quality upper-bound introduced by any mel-spectrogram vocoder compared to prior work SpeechFlow. The proposed method is evaluated on multiple speech restoration tasks, including speech denoising, bandwidth extension, codec artifact removal, and target speaker extraction. In all scenarios, finetuning our pretrained model results in superior performance over strong baselines. Notably, in the target speaker extraction task, our model outperforms existing systems, including those leveraging SSL-pretrained encoders like WavLM. The code and the pretrained checkpoints are publicly available in the NVIDIA NeMo framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16117v2-abstract-full').style.display = 'none'; document.getElementById('2409.16117v2-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> 24 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 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">5 pages, Submitted to ICASSP 2025. The implementation and configuration could be found in https://github.com/NVIDIA/NeMo/blob/main/examples/audio/conf/flow_matching_generative_ssl_pretraining.yaml The audio demo page could be found in https://kuray107.github.io/ssl_gen25-examples/index.html</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.12117">arXiv:2409.12117</a> <span> [<a href="https://arxiv.org/pdf/2409.12117">pdf</a>, <a href="https://arxiv.org/ps/2409.12117">ps</a>, <a href="https://arxiv.org/format/2409.12117">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="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Casanova%2C+E">Edresson Casanova</a>, <a href="/search/cs?searchtype=author&query=Langman%2C+R">Ryan Langman</a>, <a href="/search/cs?searchtype=author&query=Neekhara%2C+P">Paarth Neekhara</a>, <a href="/search/cs?searchtype=author&query=Hussain%2C+S">Shehzeen Hussain</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jason Li</a>, <a href="/search/cs?searchtype=author&query=Ghosh%2C+S">Subhankar Ghosh</a>, <a href="/search/cs?searchtype=author&query=Juki%C4%87%2C+A">Ante Juki膰</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sang-gil Lee</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="2409.12117v1-abstract-short" style="display: inline;"> Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modeling techniques to audio data. However, audio codecs often operate at high frame rates, resulting in slow training and inference, especially for autoregressive models. To address this challenge, we present the Low Frame-rat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12117v1-abstract-full').style.display = 'inline'; document.getElementById('2409.12117v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.12117v1-abstract-full" style="display: none;"> Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modeling techniques to audio data. However, audio codecs often operate at high frame rates, resulting in slow training and inference, especially for autoregressive models. To address this challenge, we present the Low Frame-rate Speech Codec (LFSC): a neural audio codec that leverages finite scalar quantization and adversarial training with large speech language models to achieve high-quality audio compression with a 1.89 kbps bitrate and 21.5 frames per second. We demonstrate that our novel codec can make the inference of LLM-based text-to-speech models around three times faster while improving intelligibility and producing quality comparable to previous models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12117v1-abstract-full').style.display = 'none'; document.getElementById('2409.12117v1-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 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">Submitted to ICASSP 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.03495">arXiv:2407.03495</a> <span> [<a href="https://arxiv.org/pdf/2407.03495">pdf</a>, <a href="https://arxiv.org/format/2407.03495">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> </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.2024-330">10.21437/Interspeech.2024-330 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dhawan%2C+K">Kunal Dhawan</a>, <a href="/search/cs?searchtype=author&query=Koluguri%2C+N+R">Nithin Rao Koluguri</a>, <a href="/search/cs?searchtype=author&query=Juki%C4%87%2C+A">Ante Juki膰</a>, <a href="/search/cs?searchtype=author&query=Langman%2C+R">Ryan Langman</a>, <a href="/search/cs?searchtype=author&query=Balam%2C+J">Jagadeesh Balam</a>, <a href="/search/cs?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="2407.03495v1-abstract-short" style="display: inline;"> Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech recognition (ASR), translation, speaker verification, and joint speech-text foundational models. In this work, we present a comprehensive analysis on building ASR systems with discrete codes. We investigate different method… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03495v1-abstract-full').style.display = 'inline'; document.getElementById('2407.03495v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.03495v1-abstract-full" style="display: none;"> Discrete speech representations have garnered recent attention for their efficacy in training transformer-based models for various speech-related tasks such as automatic speech recognition (ASR), translation, speaker verification, and joint speech-text foundational models. In this work, we present a comprehensive analysis on building ASR systems with discrete codes. We investigate different methods for codec training such as quantization schemes and time-domain vs spectral feature encodings. We further explore ASR training techniques aimed at enhancing performance, training efficiency, and noise robustness. Drawing upon our findings, we introduce a codec ASR pipeline that outperforms Encodec at similar bit-rate. Remarkably, it also surpasses the state-of-the-art results achieved by strong self-supervised models on the 143 languages ML-SUPERB benchmark despite being smaller in size and pretrained on significantly less data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03495v1-abstract-full').style.display = 'none'; document.getElementById('2407.03495v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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 Interspeech 2024</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of Interspeech 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.06473">arXiv:2405.06473</a> <span> [<a href="https://arxiv.org/pdf/2405.06473">pdf</a>, <a href="https://arxiv.org/format/2405.06473">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Autonomous Driving with a Deep Dual-Model Solution for Steering and Braking Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Juki%C4%87%2C+A+P">Ana Petra Juki膰</a>, <a href="/search/cs?searchtype=author&query=%C5%A0elek%2C+A">Ana 艩elek</a>, <a href="/search/cs?searchtype=author&query=Seder%2C+M">Marija Seder</a>, <a href="/search/cs?searchtype=author&query=%C5%BDarko%2C+I+P">Ivana Podnar 沤arko</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="2405.06473v1-abstract-short" style="display: inline;"> The technology of autonomous driving is currently attracting a great deal of interest in both research and industry. In this paper, we present a deep learning dual-model solution that uses two deep neural networks for combined braking and steering in autonomous vehicles. Steering control is achieved by applying the NVIDIA's PilotNet model to predict the steering wheel angle, while braking control… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06473v1-abstract-full').style.display = 'inline'; document.getElementById('2405.06473v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.06473v1-abstract-full" style="display: none;"> The technology of autonomous driving is currently attracting a great deal of interest in both research and industry. In this paper, we present a deep learning dual-model solution that uses two deep neural networks for combined braking and steering in autonomous vehicles. Steering control is achieved by applying the NVIDIA's PilotNet model to predict the steering wheel angle, while braking control relies on the use of MobileNet SSD. Both models rely on a single front-facing camera for image input. The MobileNet SSD model is suitable for devices with constrained resources, whereas PilotNet struggles to operate efficiently on smaller devices with limited resources. To make it suitable for such devices, we modified the PilotNet model using our own original network design and reduced the number of model parameters and its memory footprint by approximately 60%. The inference latency has also been reduced, making the model more suitable to operate on resource-constrained devices. The modified PilotNet model achieves similar loss and accuracy compared to the original PilotNet model. When evaluated in a simulated environment, both autonomous driving systems, one using the modified PilotNet model and the other using the original PilotNet model for steering, show similar levels of autonomous driving performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06473v1-abstract-full').style.display = 'none'; document.getElementById('2405.06473v1-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> 10 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">6 pages, 2 figures, accepted for publication in Proceedings of International Conference on Smart and Sustainable Technologies (SpliTech 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/2310.12378">arXiv:2310.12378</a> <span> [<a href="https://arxiv.org/pdf/2310.12378">pdf</a>, <a href="https://arxiv.org/format/2310.12378">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="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+T+J">Tae Jin Park</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+H">He Huang</a>, <a href="/search/cs?searchtype=author&query=Jukic%2C+A">Ante Jukic</a>, <a href="/search/cs?searchtype=author&query=Dhawan%2C+K">Kunal Dhawan</a>, <a href="/search/cs?searchtype=author&query=Puvvada%2C+K+C">Krishna C. Puvvada</a>, <a href="/search/cs?searchtype=author&query=Koluguri%2C+N">Nithin Koluguri</a>, <a href="/search/cs?searchtype=author&query=Karpov%2C+N">Nikolay Karpov</a>, <a href="/search/cs?searchtype=author&query=Laptev%2C+A">Aleksandr Laptev</a>, <a href="/search/cs?searchtype=author&query=Balam%2C+J">Jagadeesh Balam</a>, <a href="/search/cs?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="2310.12378v1-abstract-short" style="display: inline;"> We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays. The system predominantly comprises of the following integral modules: the Spea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12378v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12378v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12378v1-abstract-full" style="display: none;"> We present the NVIDIA NeMo team's multi-channel speech recognition system for the 7th CHiME Challenge Distant Automatic Speech Recognition (DASR) Task, focusing on the development of a multi-channel, multi-speaker speech recognition system tailored to transcribe speech from distributed microphones and microphone arrays. The system predominantly comprises of the following integral modules: the Speaker Diarization Module, Multi-channel Audio Front-End Processing Module, and the ASR Module. These components collectively establish a cascading system, meticulously processing multi-channel and multi-speaker audio input. Moreover, this paper highlights the comprehensive optimization process that significantly enhanced our system's performance. Our team's submission is largely based on NeMo toolkits and will be publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12378v1-abstract-full').style.display = 'none'; document.getElementById('2310.12378v1-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> CHiME-7 Workshop 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12371">arXiv:2310.12371</a> <span> [<a href="https://arxiv.org/pdf/2310.12371">pdf</a>, <a href="https://arxiv.org/format/2310.12371">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="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+T+J">Tae Jin Park</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+H">He Huang</a>, <a href="/search/cs?searchtype=author&query=Hooper%2C+C">Coleman Hooper</a>, <a href="/search/cs?searchtype=author&query=Koluguri%2C+N">Nithin Koluguri</a>, <a href="/search/cs?searchtype=author&query=Dhawan%2C+K">Kunal Dhawan</a>, <a href="/search/cs?searchtype=author&query=Jukic%2C+A">Ante Jukic</a>, <a href="/search/cs?searchtype=author&query=Balam%2C+J">Jagadeesh Balam</a>, <a href="/search/cs?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="2310.12371v1-abstract-short" style="display: inline;"> We introduce a sophisticated multi-speaker speech data simulator, specifically engineered to generate multi-speaker speech recordings. A notable feature of this simulator is its capacity to modulate the distribution of silence and overlap via the adjustment of statistical parameters. This capability offers a tailored training environment for developing neural models suited for speaker diarization… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12371v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12371v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12371v1-abstract-full" style="display: none;"> We introduce a sophisticated multi-speaker speech data simulator, specifically engineered to generate multi-speaker speech recordings. A notable feature of this simulator is its capacity to modulate the distribution of silence and overlap via the adjustment of statistical parameters. This capability offers a tailored training environment for developing neural models suited for speaker diarization and voice activity detection. The acquisition of substantial datasets for speaker diarization often presents a significant challenge, particularly in multi-speaker scenarios. Furthermore, the precise time stamp annotation of speech data is a critical factor for training both speaker diarization and voice activity detection. Our proposed multi-speaker simulator tackles these problems by generating large-scale audio mixtures that maintain statistical properties closely aligned with the input parameters. We demonstrate that the proposed multi-speaker simulator generates audio mixtures with statistical properties that closely align with the input parameters derived from real-world statistics. Additionally, we present the effectiveness of speaker diarization and voice activity detection models, which have been trained exclusively on the generated simulated datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12371v1-abstract-full').style.display = 'none'; document.getElementById('2310.12371v1-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> CHiME-7 Workshop 2023 </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> 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