  Speaker verification systems are vulnerable to spoofing attacks which
presents a major problem in their real-life deployment. To date, most of the
proposed synthetic speech detectors (SSDs) have weighted the importance of
different segments of speech equally. However, different attack methods have
different strengths and weaknesses and the traces that they leave may be short
or long term acoustic artifacts. Moreover, those may occur for only particular
phonemes or sounds. Here, we propose three algorithms that weigh
likelihood-ratio scores of individual frames, phonemes, and sound-classes
depending on their importance for the SSD. Significant improvement over the
baseline system has been obtained for known attack methods that were used in
training the SSDs. However, improvement with unknown attack types was not
substantial. Thus, the type of distortions that were caused by the unknown
systems were different and could not be captured better with the proposed SSD
compared to the baseline SSD.
