Method and apparatus for detecting spoofing conditions
An automated speaker verification (ASV) system incorporates a first deep neural network to
extract deep acoustic features, such as deep CQCC features, from a received voice sample.
The deep acoustic features are processed by a second deep neural network that classifies
the deep acoustic features according to a determined likelihood of including a spoofing
condition. A binary classifier then classifies the voice sample as being genuine or spoofed.
extract deep acoustic features, such as deep CQCC features, from a received voice sample.
The deep acoustic features are processed by a second deep neural network that classifies
the deep acoustic features according to a determined likelihood of including a spoofing
condition. A binary classifier then classifies the voice sample as being genuine or spoofed.
Method and apparatus for detecting spoofing conditions
An automated speaker verification (ASV) system incorporates a first deep neural network to
extract deep acoustic features, such as deep CQCC features, from a received voice sample.
The deep acoustic features are processed by a second deep neural network that classifies
the deep acoustic features according to a determined likelihood of including a spoofing
condition. A binary classifier then classifies the voice sample as being genuine or spoofed.
extract deep acoustic features, such as deep CQCC features, from a received voice sample.
The deep acoustic features are processed by a second deep neural network that classifies
the deep acoustic features according to a determined likelihood of including a spoofing
condition. A binary classifier then classifies the voice sample as being genuine or spoofed.
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