Deep neural network based wake-up-word speech recognition with two-stage detection

Ge, Fengpei

doi:10.1109/icassp.2017.7952659

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…1) Experimental Setup: In contrast to the previous section, a fixed vocabulary KWS task is used in this section. A Mandarin dataset similar to [4] and [27] is used in the training stage. It consists of two parts: general speech corpus and keyword specific corpus.…”

Section: B Wakeup-word Recognition In Mandarinmentioning

confidence: 99%

Sequence discriminative training for deep learning based acoustic keyword spotting

Chen

2018

Speech Communication

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Section: B Wakeup-word Recognition In Mandarinmentioning

confidence: 99%

Sequence discriminative training for deep learning based acoustic keyword spotting

Chen

2018

Speech Communication

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“…Comparing to threshold (Morgan et al, 1990;Naylor et al, 1992;Junkawitsch et al, 1997;Keshet et al, 2009;Wöllmer et al, 2009b,a;Li and Wang, 2014;Chen et al, 2014a;Gruenstein et al, 2017;Benisty et al, 2018;Myer and Tomar, 2018) Viterby (Rose and Paul, 1990;Feng and Mazor, 1992;Wilcox and Bush, 1992;Rohlicek et al, 1993;Knill and Young, 1996;Junkawitsch et al, 1997;Zheng et al, 1999;Liu et al, 2000;Vasilache and Vasilache, 2009;Tabibian et al, 2011;Leow et al, 2012;Zhu et al, 2013;Kumatani et al, 2017;Ge and Yan, 2017;Sun et al, 2017) Forward-Backward algorithm (Wilcox and Bush, 1992;Rohlicek et al, 1993) DTW (Zeppenfeld and Waibel, 1992;Kosonocky and Mammone, 1995;Kurniawati et al, 2012;Zehetner et al, 2014;Hou et al, 2016) Likelihood ratio (Jansen and Niyogi, 2009c;Szöke et al, 2010) Fuzzy logic (Manor and Greenberg, 2017) Table 10 The metrics used in studied sources.…”

Section: Decoding Approach Sourcesmentioning

confidence: 99%

“…FOM (Gish et al, 1990;Rose and Paul, 1990;Naylor et al, 1992;Zeppenfeld and Waibel, 1992;Chang and Lippmann, 1994;Gish and Ng, 1993;Rohlicek et al, 1993;Knill and Young, 1996;Junkawitsch et al, 1997;Zheng et al, 1999;Szöke et al, 2005;Lehtonen, 2005;Jansen and Niyogi, 2009a,c;Szöke et al, 2010;Tabibian et al, 2011;Bohac, 2012;Sangeetha and Jothilakshmi, 2014;Sadhu and Ghosh, 2017;Tabibian et al, 2018) EER (Szöke et al, 2010;Bohac, 2012) Accuracy (Morgan et al, 1990Ida and Yamasaki, 1998;Ge and Yan, 2017;Benisty et al, 2018;Fernández-Marqués et al, 2018) FA/kw/h (Rohlicek et al, 1989;Vroomen and Normandin, 1992;Feng and Mazor, 1992;Leow et al, 2012;Kavya and Karjigi, 2014) ROC (Marcus, 1992;Siu et al, 1994;Keshet et al, 2009;Wöllmer et al, 2009bWöllmer et al, , 2013Shokri et al, 2013;Sadhu and Ghosh, 2017;Kumatani et al, 2017) Detection rate (Feng and Mazor, 1992;Khne et al, 2004;…”

Section: Metrics Sourcesmentioning

confidence: 99%

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Kolesau¹,

Šešok²

2020

Informatica

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A large number of modern mobile devices, embedded devices and smart home devices are equipped with a voice control. Automatic recognition of the entire audio stream, however, is undesirable for the reasons of the resource consumption and privacy. Therefore, most of these devices use a voice activation system, whose task is to find the specified in advance word or phrase in the audio stream (for example, Ok, Google) and to activate the voice request processing system when it is found. The voice activation system must have the following properties: high accuracy, ability to work entirely on the device (without using remote servers), consumption of a small amount of resources (primarily CPU and RAM), noise resistance and variability of speech, as well as a small delay between the pronunciation of the key phrase and the system activation. This work is a systematic literature review on voice activation systems that satisfy the above properties. We describe the principle of various voice activation systems' operation, the characteristic representation of sound in such systems, consider in detail the acoustic modelling and, finally, describe the approaches used to assess the models' quality. In addition, we point to a number of open questions in this problem.

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“…In earlier research, a support vector machine (SVM) was used for the WUW recognition system [1]. Because the performance of deep neural network (DNN) systems has proven to be highly effective in many fields, there have been numerous efforts to build DNN-based WUW recognizers in various ways [2][3][4][5][6][7][8][9][10][11][12]. In [2], the bidirectional long short-term memory (BLSTM)-based end-to-end model was used to calculate the post-probability similar to the hybrid system, and the weighted finite-state transducers (WFSTs) were used to generate a confidence score from the calculated post-probability.…”

Section: Introductionmentioning

confidence: 99%

Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

Yoon

Kim

2020

Electronics

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This paper proposes a small-footprint wake-up-word (WUW) recognition system for real noisy environments by employing the competing-words-based feature. Competing-words-based features are generated using a ResNet-based deep neural network with small parameters using the competing-words dataset. The competing-words dataset consists of the most acoustically similar and dissimilar words to the WUW used for our system. The obtained features are used as input to the classification network, which is developed using the convolutional neural network (CNN) model. To obtain sufficient data for training, data augmentation is performed by using a room impulse response filter and adding sound signals of various television shows as background noise, which simulates an actual living room environment. The experimental results demonstrate that the proposed WUW recognition system outperforms the baselines that employ CNN and ResNet models. The proposed system shows 1.31% in equal error rate and 1.40% false rejection rate at a 1.0% false alarm rate, which are 29.57% and 50.00% relative improvements compared to the ResNet system, respectively. The number of parameters used for the proposed system is reduced by 83.53% compared to the ResNet system. These results prove that the proposed system with the competing-words-based feature is highly effective at improving WUW recognition performance in noisy environments with a smaller footprint.

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Deep neural network based wake-up-word speech recognition with two-stage detection

Cited by 27 publications

References 10 publications

Sequence discriminative training for deep learning based acoustic keyword spotting

Sequence discriminative training for deep learning based acoustic keyword spotting

Voice Activation Systems for Embedded Devices: Systematic Literature Review

Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature

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