End-to-End Residual CNN with L-GM Loss Speaker Verification System

Shi, Xuan; Du, Xingjian; Zhu, Man

doi:10.1109/icdsp.2018.8631697

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Deep residual learning has been successfully applied to different tasks like noise-robust speech recognition [26] and speaker verification [27].…”

Section: A Deep Residual Learningmentioning

confidence: 99%

Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices

López-Espejo

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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For certain applications, keyword spotting (KWS) requires some degree of personalization. This is the case for KWS for hearing assistive devices, e.g., hearing aids, where only the device user should be allowed to trigger the KWS system. In this paper, we first develop a new realistic hearing aid experimental framework. Next, using this framework we show that the performance of a state-of-the-art multi-task deep learning architecture exploiting cepstral features for joint KWS and users' own-voice/external speaker detection drops significantly. To overcome this problem, we use phase difference information through GCC-PHAT (Generalized Cross-Correlation with PHAse Transform)-based coefficients along with log-spectral magnitude features. In addition, we demonstrate that working in the perceptually-motivated constant-Q transform (CQT) domain instead of in the short-time Fourier transform (STFT) domain allows for the generation of compact and coherent features which provide superior KWS performance. Our experimental results show that our CQT-based proposal achieves a relative KWS accuracy improvement of around 18% compared to using cepstral features while dramatically decreasing the number of multiplications in the multi-task architecture, which is key in the context of low-resource devices like hearing assistive devices.

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“…Deep residual learning has been successfully applied to different tasks like noise-robust speech recognition [26] and speaker verification [27].…”

Section: A Deep Residual Learningmentioning

confidence: 99%

Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices

López-Espejo

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…In the first, the network is trained as a multi-class classification network that classifies a large number of classes (speakers in our case). These networks use objectives that include, in addition to traditional classification losses, augmentations that are intended to encourage enhanced within-class clustering of embeddings [3,24,25,26,27,28,29] along with increased separation of embeddings of instances from different classes. The expectation is that this behavior will generalize to data outside the training set.…”

Section: Introductionmentioning

confidence: 99%

Masked Proxy Loss For Text-Independent Speaker Verification

Lian,

Kumar,

Dhamyal

et al. 2020

Preprint

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Open-set speaker recognition can be regarded as a metric learning problem, which is to maximize inter-class variance and minimize intra-class variance. Supervised metric learning can be categorized into entity-based learning and proxybased learning 1 . Most of existing metric learning objectives like Contrastive, Triplet, Prototypical, GE2E, etc all belong to the former division, the performance of which is either highly dependent on sample mining strategy or restricted by insufficient label information in the mini-batch. Proxy-based losses mitigate both shortcomings, however, fine-grained connections among entities are either not or indirectly leveraged. This paper proposes a Mask Proxy (MP) loss which directly incorporates both proxy-based relationship and entity-based relationship. We further propose Multinomial Mask Proxy (MMP) loss to leverage the hardness of entity-to-entity pairs. These methods have been applied to evaluate on VoxCeleb test set and reach state-of-the-art Equal Error Rate(EER).

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