November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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Feature extraction module
In order to make the computation easy, the incoming audio signal is run through a voice-activity detection system and the signal is divided into speech and non-speech parts of the signals. The voice activity detector uses a 30-component diagonal covariance GMM model. The input to this model is 13-dimensional PLP features, their deltas, and double deltas. The output of GMM is passed to a State Machine that does temporal smoothing.
The output of this GMM-SM module is speech and non-speech parts of the signal.
The speech parts of the signal are further processed to generate the features. The acoustic features are generated based on 40-dimensional log-filterbank energies computed every 10 ms over a window of 25 ms. 10 ...
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