AUC Optimization for Deep Learning Based Voice Activity Detection

Fan, Zichen; Bai, Zhongxin; Zhang, Xiaolei; Rahardja, Susanto; Chen, Jingdong

doi:10.1109/icassp.2019.8682803

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…A deep neural network (DNN) approach is used in [8] to perform SAD in a multi-room environment. In [9], new optimisation techniques based on the area under the ROC curve are explored in the framework of a deep learning SAD system.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains

et al. 2022

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Speech activity detection (SAD) aims to accurately classify audio fragments containing human speech. Current state-of-the-art systems for the SAD task are mainly based on deep learning solutions. These applications usually show a significant drop in performance when test data are different from training data due to the domain shift observed. Furthermore, machine learning algorithms require large amounts of labelled data, which may be hard to obtain in real applications. Considering both ideas, in this paper we evaluate three unsupervised domain adaptation techniques applied to the SAD task. A baseline system is trained on a combination of data from different domains and then adapted to a new unseen domain, namely, data from Apollo space missions coming from the Fearless steps challenge. Experimental results demonstrate that domain adaptation techniques seeking to minimise the statistical distribution shift provide the most promising results. In particular, Deep CORAL method reports a 13% relative improvement in the original evaluation metric when compared to the unadapted baseline model. Further experiments show that the cascaded application of Deep CORAL and pseudo-labelling techniques can improve even more the results, yielding a significant 24% relative improvement in the evaluation metric when compared to the baseline system.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains

et al. 2022

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“…A deep neural network approach is used in [8] to perform SAD in a multi-room environment. In [9], new optimisation techniques based on the area under the ROC curve are explored in the framework of a deep learning SAD system.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Representation Learning for Speech Activity Detection in the Fearless Steps Challenge 2021

Gimeno¹,

Ortega²,

Miguel³

et al. 2021

Interspeech 2021

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In this paper, we describe the ViVoLab speech activity detection (SAD) system submitted to the Fearless steps Challengephase III. This series of challenges have proposed a number of speech processing task dealing with audio from Apollo space missions over the last few years. The focus in this edition is set on the generalisation capabilities of the systems, with new evaluation data from different channels. Our proposed submission is based on the use of the unsupervised representation learning paradigm, seeking to obtain a new and more discriminative audio representation than traditional perceptual features such as log Mel-filterbank energies. These new features are used to train different variations of a convolutional recurrent neural network (CRNN). Experimental results show that features learned via unsupervised learning provide a much more robust representation, significantly reducing the mismatch observed between development and evaluation partition results. Obtained results largely outperform the organisation baseline, achieving a DCF metric of 2.98% on the evaluation set and ranking third among all the participant teams.

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“…The submitted SAD system is based on the multilayer perceptron (MLP), which has been shown to be simple and efficient [5,6,7]. We significantly improved the performance by tuning post-processing parameters, and experimented with untranscribed data by means of ASR transcriptions of the 19k hour Apollo-11 (A11) dataset.…”

Section: Introductionmentioning

confidence: 99%

“This is Houston. Say again, please”. The Behavox System for the Apollo-11 Fearless Steps Challenge (Phase II)

Gorin

Kulko²,

Grima³

et al. 2020

Interspeech 2020

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We describe the speech activity detection (SAD), speaker diarization (SD), and automatic speech recognition (ASR) experiments conducted by the Behavox team for the Interspeech 2020 Fearless Steps Challenge (FSC-2). A relatively small amount of labeled data, a large variety of speakers and channel distortions, specific lexicon and speaking style resulted in high error rates on the systems which involved this data. In addition to approximately 36 hours of annotated NASA mission recordings, the organizers provided a much larger but unlabeled 19k hour Apollo-11 corpus that we also explore for semi-supervised training of ASR acoustic and language models, observing more than 17% relative word error rate improvement compared to training on the FSC-2 data only. We also compare several SAD and SD systems to approach the most difficult tracks of the challenge (track 1 for diarization and ASR), where long 30-minute audio recordings are provided for evaluation without segmentation or speaker information. For all systems, we report substantial performance improvements compared to the FSC-2 baseline systems, and achieved a first-place ranking for SD and ASR and fourth-place for SAD in the challenge.

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AUC Optimization for Deep Learning Based Voice Activity Detection

Cited by 18 publications

References 25 publications

Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains

Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains

Unsupervised Representation Learning for Speech Activity Detection in the Fearless Steps Challenge 2021

“This is Houston. Say again, please”. The Behavox System for the Apollo-11 Fearless Steps Challenge (Phase II)

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