June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Features extraction in ASR
Features extraction is an important preprocessing stage in a DL pipeline of ASR. This stage consists of an analyzer and the extraction of audio fingerprints or features. This stage also mainly computes a sequence of feature vectors, which provides a compact representation of a gathered speech signal. Generally, this task can be performed in three key steps. The first step is known as speech analysis. This step carries out a spectra-temporal analysis of the speech signal and generates raw features describing the envelope of the power spectrum of short speech intervals. The second step extracts an extended feature vector that consists of static and dynamic features. The final step converts these extended feature vectors ...
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