Model Shrinking for Embedded Keyword Spotting

Sun, Ming; Nagaraja, Varun; Hoffmeister, Björn; Vitaladevuni, Shiv

doi:10.1109/icmla.2015.121

Cited by 17 publications

(8 citation statements)

References 20 publications

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“…one-hot vector) without considering them as sequences of characters. Other recent works aim to spot specific keywords used to activate voice assistant systems [29,30,31]. The application of BiLSTMs on KWS was first proposed in [32].…”

Section: Related Workmentioning

confidence: 99%

Zero-Shot Keyword Spotting for Visual Speech Recognition In-the-wild

Stafylakis

Tzimiropoulos

2018

Computer Vision – ECCV 2018

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Visual keyword spotting (KWS) is the problem of estimating whether a text query occurs in a given recording using only video information. This paper focuses on visual KWS for words unseen during training, a real-world, practical setting which so far has received no attention by the community. To this end, we devise an end-to-end architecture comprising (a) a state-of-the-art visual feature extractor based on spatiotemporal Residual Networks, (b) a grapheme-to-phoneme model based on sequence-to-sequence neural networks, and (c) a stack of recurrent neural networks which learn how to correlate visual features with the keyword representation. Different to prior works on KWS, which try to learn word representations merely from sequences of graphemes (i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder model which learns how to map words to their pronunciation. We demonstrate that our system obtains very promising visual-only KWS results on the challenging LRS2 database, for keywords unseen during training. We also show that our system outperforms a baseline which addresses KWS via automatic speech recognition (ASR), while it drastically improves over other recently proposed ASR-free KWS methods.

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Section: Related Workmentioning

confidence: 99%

Zero-Shot Keyword Spotting for Visual Speech Recognition In-the-wild

Stafylakis

Tzimiropoulos

2018

Computer Vision – ECCV 2018

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“…Recently, with increasing popularity of voice assistant systems, small-footprint keyword spotting systems have been attracting more attention [4,5,6]. For example, Alexa on Amazon Tap requires a keyword spotting system running continuously on the device under tight CPU, memory, latency and power usage constraints.…”

Section: Introductionmentioning

confidence: 99%

Compressed Time Delay Neural Network for Small-Footprint Keyword Spotting

et al. 2017

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In this paper we investigate a time delay neural network (TDNN) for a keyword spotting task that requires low CPU, memory and latency. The TDNN is trained with transfer learning and multi-task learning. Temporal subsampling enabled by the time delay architecture reduces computational complexity. We propose to apply singular value decomposition (SVD) to further reduce TDNN complexity. This allows us to first train a larger full-rank TDNN model which is not limited by-CPU/memory constraints. The larger TDNN usually achieves better performance. Afterwards, its size can be compressed by SVD to meet the budget requirements. Hidden Markov models (HMM) are used in conjunction with the networks to perform keyword detection and performance is measured in terms of area under the curve (AUC) for detection error tradeoff (DET) curves. Our experimental results on a large in-house far-field corpus show that the full-rank TDNN achieves a 19.7% DET AUC reduction compared to a similar-size deep neural network (DNN) baseline. If we train a larger size full-rank TDNN first and then reduce it via SVD to the comparable size of the DNN, we obtain a 37.6% reduction in DET AUC compared to the DNN baseline.

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“…Gaussian mixture model (GMM) is a traditional one for modeling observation probabilities of HMM. With deep neural network (DNN) gradually takes the place of GMM in speech recognition, DNN and other neural network based models are also attempted in this structure [7,8]. In recent years, the systems that only use neural network without HMM involved are proposed [9,10].…”

Section: Introductionmentioning

confidence: 99%

Compact Feedforward Sequential Memory Networks for Small-footprint Keyword Spotting

Chen¹,

Zhang

Lei

et al. 2018

Interspeech 2018

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Due to limited resource on devices and complicated scenarios, a compact model with high precision, low computational cost and latency is expected for small-footprint keyword spotting tasks. To fulfill these requirements, in this paper, compact Feedforward Sequential Memory Network (cFSMN) which combines low-rank matrix factorization with conventional FSMN is investigated for a far-field keyword spotting task. The effect of its architecture parameters is analyzed. Towards achieving lower computational cost, multiframe prediction (MFP) is applied to cFSMN. For enhancing the modeling capacity, an advanced MFP is attempted by inserting small DNN layers before output layers. The performance is measured by area under the curve (AUC) for detection error tradeoff (DET) curves. The experiments show that compared with a well-tuned long short-term memory (LSTM) which needs the same latency and twofold computational cost, the cFSMN achieves 18.11% and 29.21% AUC relative decreases on the test sets which are recorded in quiet and noisy environment respectively. After applying advanced MFP, the system gets 0.48% and 20.04% AUC relative decrease over conventional cFSMN on the quiet and noisy test sets respectively, while the computational cost relatively reduces 46.58%.

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Model Shrinking for Embedded Keyword Spotting

Cited by 17 publications

References 20 publications

Zero-Shot Keyword Spotting for Visual Speech Recognition In-the-wild

Zero-Shot Keyword Spotting for Visual Speech Recognition In-the-wild

Compressed Time Delay Neural Network for Small-Footprint Keyword Spotting

Compact Feedforward Sequential Memory Networks for Small-footprint Keyword Spotting

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