Abstract: Computer-implemented detection of anomalous telephone calls, for example detection of interconnect bypass fraud, is disclosed. A telephone call associated with user devices is analyzed remote from the user devices. A first set of multiple features, for example Mel Frequency Cepstral Coefficients, is derived from a call audio stream. The first set is converted to an embedding vector, for example via a model based on a Universal Background Model comprising a Gaussian Mixture Model, which model is preferably configured based on a training plurality of first sets of multiple features derived form a corresponding training plurality of audio streams. Occurrence, or probability of occurrence, of an anomalous telephone call is determined based on the embedding vector, for example via a back-end classifier, such as a Gaussian Backend Model, which classifier is preferably configured based on labels associated with the training plurality of audio streams.

Abstract: Computer-implemented detection of anomalous telephone calls, for example detection of interconnect bypass fraud, is disclosed. A telephone call associated with user devices is analyzed remote from the user devices. A first set of multiple features, for example Mel Frequency Cepstral Coefficients, is derived from a call audio stream. The first set is converted to an embedding vector, for example via a model based on a Universal Background Model comprising a Gaussian Mixture Model, which model is preferably configured based on a training plurality of first sets of multiple features derived form a corresponding training plurality of audio streams. Occurrence, or probability of occurrence, of an anomalous telephone call is determined based on the embedding vector, for example via a back-end classifier, such as a Gaussian Backend Model, which classifier is preferably configured based on labels associated with the training plurality of audio streams.