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About Deep Learning For Spoken Language Identification: GR Egoire Montavon
The document summarizes research on using deep learning for spoken language identification from audio clips. A deep neural network with convolutional and pooling layers (CNN-TDNN) is implemented to automatically learn features from spectrograms of speech samples, without relying on hand-coded features. The network is evaluated on two datasets of English, French, and German speech clips. On both datasets, the deep network achieves substantially higher identification accuracy compared to a shallow network, especially when identifying languages from new speakers or radio stations not in the training data.
Detailed Information
| Author: | ['Maged Hamouda'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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