The 2020 Personalized Voice Trigger Challenge: Open Datasets, Evaluation Metrics, Baseline System and Results

Jia, Yan; Wang, Xingming; Qin, Xiaoyi; Zhang, Yinping; Wang, Xuyang; Wang, Junjie; Zhang, Dong; Li, Ming

doi:10.21437/interspeech.2021-602

Cited by 4 publications

(1 citation statement)

References 25 publications

(23 reference statements)

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“…A typical approach is to cascade speaker independent voice trigger detection and speaker verification [1][2][3][4]. A universal voice trigger detector is trained on speech signals from various speakers to perform speaker independent voice trigger detection, then speaker verification is performed by a speaker recognition model exploiting enrollment utterances spoken by the target user.…”

Section: Introductionmentioning

confidence: 99%

Improving Voice Trigger Detection with Metric Learning

Nayak¹,

Higuchi²,

Gupta³

et al. 2022

Preprint

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Voice trigger detection is an important task, which enables activating a voice assistant when a target user speaks a keyword phrase. A detector is typically trained on speech data independent of speaker information and used for the voice trigger detection task. However, such a speaker independent voice trigger detector typically suffers from performance degradation on speech from underrepresented groups, such as accented speakers. In this work, we propose a novel voice trigger detector that can use a small number of utterances from a target speaker to improve detection accuracy. Our proposed model employs an encoder-decoder architecture. While the encoder performs speaker independent voice trigger detection, similar to the conventional detector, the decoder is trained with metric learning and predicts a personalized embedding for each utterance. A personalized voice trigger score is then obtained as a similarity score between the embeddings of enrollment utterances and a test utterance. The personalized embedding allows adapting to target speaker's speech when computing the voice trigger score, hence improving voice trigger detection accuracy. Experimental results show that the proposed approach achieves a 38% relative reduction in a false rejection rate (FRR) compared to a baseline speaker independent voice trigger model.

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Section: Introductionmentioning

confidence: 99%

Improving Voice Trigger Detection with Metric Learning

Nayak¹,

Higuchi²,

Gupta³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Two-stage streaming keyword detection and localization with multi-scale depthwise temporal convolution

2022

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Deep Spoken Keyword Spotting: An Overview

López-Espejo¹,

Hansen²,

Jensen³

2022

IEEE Access

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Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS.INDEX TERMS Keyword spotting, deep learning, acoustic model, small footprint, robustness.

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The 2020 Personalized Voice Trigger Challenge: Open Datasets, Evaluation Metrics, Baseline System and Results

Cited by 4 publications

References 25 publications

Improving Voice Trigger Detection with Metric Learning

Improving Voice Trigger Detection with Metric Learning

Two-stage streaming keyword detection and localization with multi-scale depthwise temporal convolution

Deep Spoken Keyword Spotting: An Overview

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