An effective voiceprint based identity authentication system for Mandarin smartphone users

Junhong Liu, Yuexian Zou, Yichi Huang

Abstract: Voiceprint based identity authentication system (IAS) for smartphone users is highly demanded in mobile internet times. There are some successful application cases for English smartphone users. However, to our knowledge, the research outcomes are few for Mandarin smartphone users. Analysis shows that there remain some issues need to be carefully considered: (1) security issue: vulnerable to replay attacks; (2) user experience issue: zero-tolerance of misreading; (3) channel mismatch issue: perform poorly when user change his smartphone. Taking above issues into account, this study strives to develop an effective voiceprint based IAS (termed as DR-EiSV-IAS) for Mandarin smartphone users. Specifically, a content disorder degree (CDD) module implemented with DNN based digit recognition is introduced to resist replay attacks and enhance the fault-tolerance of misreading. Besides, the speaker verification is carefully designed using enhanced ivector technique where ivector framework is incorporated with WCCN to compensate for channel variability. To facilitate this study, we have built up a Mandarin corpus MTDSR2015, which is the first public and free Mandarin database recorded by smartphones for text-dependent speaker recognition research. Extensive experiments have been conducted on both MTDSR2015 and RSR2015 to validate the effectiveness of our proposed DR-EiSV-IAS.