A Voice Spam Filter to Clean Subscribers' Mailbox

With the growing popularity of VoIP and its large customer base, the incentives of telemarketers for voice spam has been increasing in the recent years. If the threat of voice spam remains unchecked, it could become a problem as serious as email spam today. Compared to email spam, voice spam will be much more obnoxious and time consuming nuisance for telephone subscribers to filter out. In this paper, we propose a content-based approach to protect telephone subscribers voice mailboxes from voice spam. In particular, based on Dynamic Time Warping (DTW), we develop a speaker independent speech recognition system to make content comparison of speech messages. Using our system, the voice messages left on the media server by callers are matched against a set of spam filtering rules involving the study of call behavioral pattern and the analysis of message content. The uniqueness of our spam filtering approach lies in its independence on the generation of voice spam, regardless whether spammers play same spam content recorded in many different ways, such as human or machine generated voice, male or female voice, and different accents. We validate the efficacy of the proposed scheme through real experiments, and our experimental results show that it can effectively filter out spam from the subscribers’ voice mailbox with 0.67% false positive rate and 8.33% false negative rate.