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.

A processor-implemented method with data recognition includes: extracting input feature data from input data; calculating a matching score between the extracted input feature data and enrolled feature data of an enrolled user, based on the extracted input feature data, common component data of a plurality of enrolled feature data corresponding to the enrolled user, and distribution component data of the plurality of enrolled feature data corresponding to the enrolled user; and recognizing the input data based on the matching score.

A processor-implemented method with data recognition includes: extracting input feature data from input data; calculating a matching score between the extracted input feature data and enrolled feature data of an enrolled user, based on the extracted input feature data, common component data of a plurality of enrolled feature data corresponding to the enrolled user, and distribution component data of the plurality of enrolled feature data corresponding to the enrolled user; and recognizing the input data based on the matching score.

Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate language independent-speaker verification. In one aspect, a method includes actions of receiving, by a user device, audio data representing an utterance of a user. Other actions may include providing, to a neural network stored on the user device, input data derived from the audio data and a language identifier. The neural network may be trained using speech data representing speech in different languages or dialects. The method may include additional actions of generating, based on output of the neural network, a speaker representation and determining, based on the speaker representation and a second representation, that the utterance is an utterance of the user. The method may provide the user with access to the user device based on determining that the utterance is an utterance of the user.

Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate language independent-speaker verification. In one aspect, a method includes actions of receiving, by a user device, audio data representing an utterance of a user. Other actions may include providing, to a neural network stored on the user device, input data derived from the audio data and a language identifier. The neural network may be trained using speech data representing speech in different languages or dialects. The method may include additional actions of generating, based on output of the neural network, a speaker representation and determining, based on the speaker representation and a second representation, that the utterance is an utterance of the user. The method may provide the user with access to the user device based on determining that the utterance is an utterance of the user.

An account adding method is performed by a social networking application running at a mobile terminal when communicating with a second terminal (e.g., using a chat session). The method includes: recording voice information from the second terminal using the social networking application; extracting character string information and voiceprint information from the voice information; sending the character string information and the voiceprint information to a server; receiving an account that matches the character string information and the voiceprint information and that is sent by the server; and adding the account to a contact list of the social networking application. For example, the social networking application is started before starting a telephone call with the second terminal and the voice information is recorded during the telephone call.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

A method including receiving an incoming call from a calling device of a caller and determining identification information for the calling device. The method also includes receiving voice audio data of the caller from the calling device, converting the voice audio data to caller phones, and identifying a customer account associated with the identification information. The method further includes obtaining user phones for multiple candidate users associated with the identified customer account, comparing the caller phones to the user phones for the multiple candidate users, and determining the identity of the caller based on the comparison.

In one embodiment, a method includes, by one or more computing devices of an online social network, receiving, from a client system at a first location, an audio input from an unknown user, identifying a first user who is proximate to the first location, identifying the unknown user as a second user based on a comparison of the audio input to one or more voiceprints of one or more candidate users accessible by the client system, respectively, wherein each voiceprint comprises audio data for auditory identification of a unique user, and wherein each candidate user is a contact of the first user, and sending customized content to one or more of the first user or the second user, wherein the content is customized using interest information associated with the first or second user.