A lightweight convolutional neural network for KWS
Keyword spotting has been widely used in smart homes and mobile devices, where the goal is to achieve high accuracy with low latency and small computational effort. Convolutional neural networks are not only performed well in extracting speech features, but also show good robustness in the face of n...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/158686 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Keyword spotting has been widely used in smart homes and mobile devices, where the goal is to achieve high accuracy with low latency and small computational effort. Convolutional neural networks are not only performed well in extracting speech features, but also show good robustness in the face of noise interference.
This dissertation proposes a convolutional neural network using Google's speech command as the dataset, with a trade-off between high accuracy and small memory. Existing techniques were applied to modify the network to obtain the results of the best performing model.
Further, the training set is noise-loaded to show the effect of noise on the audio files, and the performance of the KWS-Net under three levels of noise is demonstrated.
Finally, some advanced deep learning networks are used for speech recognition experiments on the same dataset, including ResNet, DenseNet, MobileNet. The results section compares the test results of the proposed network with these advanced networks in speech recognition, highlighting the good performance achieved by the KWS-Net in terms of high accuracy and low number of parameters. |
|---|