| Title |
An Intelligent Speech Multifeature Recognition Method Based on Deep Machine Learning: A Smart City Application |
| ID_Doc |
41978 |
| Authors |
Song, Y; Yan, K |
| Title |
An Intelligent Speech Multifeature Recognition Method Based on Deep Machine Learning: A Smart City Application |
| Year |
2024 |
| Published |
Journal Of Testing And Evaluation, 52, 3 |
| DOI |
10.1520/JTE20220686 |
| Abstract |
Speech recognition has the problem of low recognition accuracy because of poor denoising effect and low endpoint detection accuracy. Therefore, a new intelligent speech multifeature recognition method based on deep machine learning is proposed. In this method, speech signals are digitally processed, a first-order finite impulse response (FIR) high pass digital filter is used to preemphasize digital speech signals, and short-term energy and zero crossing rate are combined to detect speech signals to expand endpoints. The detected speech signal is input into the depth autoencoder, and the features of the speech signal are extracted through deep learning. The Gaussian mixture model of deep machine learning is constructed using a continuous distribution hidden Markov model, and the extracted features are input into the model to complete feature recognition. The experimental results show that the proposed method has high endpoint detection accuracy, good denoising effect, and high recognition accuracy, and this method has higher application value. |
| Author Keywords |
deep machine learning; first-order FIR high pass digital filter; deep autoencoder; endpoint detec-tion; Gaussian mixture model; speech feature recognition |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001068708200001 |
| WoS Category |
Materials Science, Characterization & Testing |
| Research Area |
Materials Science |
| PDF |
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