EdgeCRNN: an edge-computing oriented model of acoustic feature enhancement for keyword spotting

Wei, Yungen; Gong, Zheng; Yang, Shunzhi; Yang, Kai; Yan, Wen

doi:10.1007/s12652-021-03022-1

Cited by 12 publications

(2 citation statements)

References 32 publications

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“…MatchboxNet [38] is a deep residual network composed from 1D time-channel separable convolution, batch-norm layers, ReLU and dropout layers. Inspired by [37] and [38], EdgeCRNN [39] was proposed, an edgecomputing oriented model of acoustic feature enhancement for keyword spotting. Recently, [40] combined a triplet lossbased embedding and a variant of K-Nearest Neighbor (KNN) for classification.…”

Section: Related Workmentioning

confidence: 99%

Speech Augmentation Based Unsupervised Learning for Keyword Spotting

Wang¹,

Cheng²,

Tang³

et al. 2022

Preprint

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In this paper, we investigated a speech augmentation based unsupervised learning approach for keyword spotting (KWS) task. KWS is a useful speech application, yet also heavily depends on the labeled data. We designed a CNN-Attention architecture to conduct the KWS task. CNN layers focus on the local acoustic features, and attention layers model the longtime dependency. To improve the robustness of KWS model, we also proposed an unsupervised learning method. The unsupervised loss is based on the similarity between the original and augmented speech features, as well as the audio reconstructing information. Two speech augmentation methods are explored in the unsupervised learning: speed and intensity. The experiments on Google Speech Commands V2 Dataset demonstrated that our CNN-Attention model has competitive results. Moreover, the augmentation based unsupervised learning could further improve the classification accuracy of KWS task. In our experiments, with augmentation based unsupervised learning, our KWS model achieves better performance than other unsupervised methods, such as CPC, APC, and MPC.

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Section: Related Workmentioning

confidence: 99%

Speech Augmentation Based Unsupervised Learning for Keyword Spotting

Wang¹,

Cheng²,

Tang³

et al. 2022

Preprint

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show abstract

“…To provide an economical alternative and increase performance [6], the unique depthwise separable convolutional neural network (DS-CNN) [6], [11], [14], [15] was introduced for the embedded KWS. CNN is altered on each layer of convolution, beginning with the second layer, which includes depthwise and pointwise convolutions, as well as a batch normalization layer with ReLU activation.…”

Section: Introductionmentioning

confidence: 99%

Keyword Spotting System With Nano 33 Ble Sense Using Embedded Machine Learning Approach

Abbas

Ahmad

2023

Jurnal Teknologi

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Due to the obvious advancement of artificial intelligence, keyword spotting has become a fast-growing technology that was first launched a few years ago by hidden Markov models. Keyword spotting is the technique of finding terms that have been pre-programmed into a machine learning model. However, because the keyword spotting system model will be installed on a small and resource-constrained device, it must be minimal in size. It is difficult to maintain accuracy and performance when minimizing the model size. We suggested in this paper to develop a TinyML model that responds to voice commands by detecting words that are utilized in a cascade architecture to start or control a program. The keyword detection machine learning model was built, trained, and tested using the edge impulse development platform. The technique follows the model-building workflow, which includes data collection, preprocessing, training, testing, and deployment. 'On,' 'Off,' noise, and unknown databases were obtained from the Google speech command database V1 and applied for training and testing. The MFCC was used to extract features and CNN was used to generate the model, which was then optimized and deployed on the microcontroller. The model's evaluation represents an accuracy of 84.51% based on the datasets. Finally, the KWS was successfully implemented and assessed on Arduino Nano 33 BLE Sense for two studies in terms of accuracy at three different times and by six different persons.

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Learning Efficient Representations for Keyword Spotting with Triplet Loss

Vygon

Mikhaylovskiy

2021

Lecture Notes in Computer Science

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In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most notably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also match the current best published SOTA for Google Speech Commands dataset V2 10+2-class classification with an architecture that is about 6 times more compact and improve the current best published SOTA for 35class classification on Google Speech Commands dataset V2 by over 40%. 1

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EdgeCRNN: an edge-computing oriented model of acoustic feature enhancement for keyword spotting

Cited by 12 publications

References 32 publications

Speech Augmentation Based Unsupervised Learning for Keyword Spotting

Speech Augmentation Based Unsupervised Learning for Keyword Spotting

Keyword Spotting System With Nano 33 Ble Sense Using Embedded Machine Learning Approach

Learning Efficient Representations for Keyword Spotting with Triplet Loss

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