140 results about "Speaker verification" patented technology

Typical speaker verification systems usually employ speakers' audio data collected during an enrollment phase when users enroll with the system and provide respective voice samples. Due to technical, business, or other constraints, the enrollment data may not be large enough or rich enough to encompass different inter-speaker and intra-speaker variations. According to at least one embodiment, a method and apparatus employing classifier adaptation based on field data in a deployed voice-based interactive system comprise: collecting representations of voice characteristics, in association with corresponding speakers, the representations being generated by the deployed voice-based interactive system; updating parameters of the classifier, used in speaker recognition, based on the representations collected; and employing the classifier, with the corresponding parameters updated, in performing speaker recognition.

In one aspect, a method for determining a validity of an identity asserted by a speaker using a voice print that models speech of a user whose identity the speaker is asserting is provided. The method comprises acts of performing a first verification stage comprising acts of obtaining a first voice signal from the speaker uttering at least one first challenge utterance; and comparing at least one characteristic feature of the first voice signal with at least a portion of the voice print to assess whether the at least one characteristic feature of the first voice signal is similar enough to the at least a portion of the voice print to conclude that the first voice signal was obtained from an utterance by the user. The method further comprises performing a second verification stage if it is concluded in the first verification stage that the first voice signal was obtained from an utterance by the user, the second verification stage comprising acts of adapting at least one parameter of the voice print based, at least in part, on the first voice signal to obtain an adapted voice print, obtaining a second voice signal from the speaker uttering at least one second challenge utterance, and comparing at least one characteristic feature of the second voice signal with at least a portion of the adapted voice print to assess whether the at least one characteristic feature of the second voice signal is similar enough to the at least a portion of the adapted voice print to conclude that the second voice signal was obtained from an utterance by the user.

A device includes a memory, a receiver, a processor, and a display. The memory is configured to store a speaker model. The receiver is configured to receive an input audio signal. The processor is configured to determine a first confidence level associated with a first portion of the input audio signal based on the speaker model. The processor is also configured to determine a second confidence level associated with a second portion of the input audio signal based on the speaker model. The display is configured to present a graphical user interface associated with the first confidence level or associated with the second confidence level.

A text-independent speaker verification system utilizes mel frequency cepstral coefficients analysis in the feature extraction blocks, template modeling with vector quantization in the pattern matching blocks, an adaptive threshold and an adaptive decision verdict and is implemented in a stand-alone device using less powerful microprocessors and smaller data storage devices than used by comparable systems of the prior art.

A method for controlling subscription services delivered to a user by an Internet Service Provider (ISP) coupled to an Automated Intelligent Network (AIN) telephone system with at least one central office switching system. An intelligent peripheral subsystem is connected to the central office switching system, via a call connection channel. The intelligent peripheral subsystem providing at least one auxiliary call processing capability via the call connection channel and provides a telephony speaker authentication method for selectively authorizing updates to user subscription services types provided by the ISP, wherein each of the subscription services types include one or more service options. The method for controlling subscription services including: a) prompting for a voice response; b) authenticating a user's voice response pattern by performing a speaker-dependent cepstrum matching algorithm with previously stored samples of the user's voice response, and if the user's voice response pattern is positively authenticated with previously stored samples, then performing the steps of: c) presenting a menu of user subscription types, each with available AIN service options; and d) responding to a user selection of at least one user subscription types, each with service option, by activating a service option selected by the user, wherein the service option is governed by said ISP by regulating one or more Uniform Resource Locator (URL) addresses received by a user subscriber device from said ISP over a telecommunication line.

A method of verifying a user identity using a Web-based multimodal interface can include sending, to a remote computing device, a multimodal markup language document that, when rendered by the remote computing device, queries a user for a user identifier and causes audio of the user's voice to be sent to a multimodal, Web-based application. The user identifier and the audio can be received at about a same time from the client device. The audio can be compared with a voice print associated with the user identifier. The user at the remote computing device can be selectively granted access to the system according to a result obtained from the comparing step.

A voice controlled voice-messaging systems permits manipulation of voice messages over the telephone without the need for keypressed commands. Manipulation includes playback of messages, deletion of messages, saving messages, and sending a new message to a recipient from a user-defined list. Access to the messaging system is by spoken password. Voice input also allows the following functions: enrollment of a spoken password; granting or refusing user access to the system based on verification of the user speaking a password; recognition of spoken utterances of "yes" and "no" in response to prompts by the VMS; recognition of "stop" to interrupt and stop a voice message during playback; enrollment of a spoken personal directory of names of potential message recipients; and recognition of names in the spoken personal directory. The invention uses speaker verification, speaker-independent recognition (SIR) of "yes/no", speaker-independent recognition of "stop" during message playback, and speaker-dependent recognition (SDR) of a limited vocabulary of short phrases such as a list of message recipients' names.

A method and system for speech characterization. One embodiment includes a method for speaker verification which includes collecting data from a speaker, wherein the data comprises acoustic data and non-acoustic data. The data is used to generate a template that includes a first set of “template” parameters. The method further includes receiving a real-time identity claim from a claimant, and using acoustic data and non-acoustic data from the identity claim to generate a second set of parameters. The method further includes comparing the first set of parameters to the set of parameters to determine whether the claimant is the speaker. The first set of parameters and the second set of parameters include at least one purely non-acoustic parameter, including a non-acoustic glottal shape parameter derived from averaging multiple glottal cycle waveforms.

In one aspect, a method for determining validity of an identity asserted by a speaker using a voice print associated with a user whose identity the speaker is asserting, the voice print obtained from characteristic features of at least one first voice signal obtained from the user uttering at least one enrollment utterance including at least one enrollment word is provided. The method comprises acts of obtaining a second voice signal of the speaker uttering at least one challenge utterance, wherein the at least one challenge utterance includes at least one word that was not in the at least one enrollment utterance, obtaining at least one characteristic feature from the second voice signal, comparing the at least one characteristic feature with at least a portion of the voice print to determine a similarity between the at least one characteristic feature and the at least a portion of the voice print, and determining whether the speaker is the user based, at least in part, on the similarity between the at least one characteristic feature and the at least a portion of the voice print.

A method and device for voiceprint recognition, include: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a respective high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the respective high-level voiceprint feature sequence registered for the user.

A method and system (800) for voiceprint recognition, include: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a respective high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the respective high-level voiceprint feature sequence registered for the user.

Methods and system for authenticating a user are disclosed. The present invention includes accessing a collection of personal information related to the user. The present invention also includes performing an authentication operation that is based on the collection of personal information. The authentication operation incorporates at least one dynamic component and prompts the user to give an audible utterance. The audible utterance is compared to a stored voiceprint.

A method for unsupervised environmental normalization for speaker verification using hierarchical clustering is disclosed. Training data (speech samples) are taken from T enrolled (registered) speakers over any one of M channels, e.g., different microphones, communication links, etc. For each speaker, a speaker model is generated, each containing a collection of distributions of audio feature data derived from the speech sample of that speaker. A hierarchical speaker model tree is created, e.g., by merging similar speaker models on a layer by layer basis. Each speaker is also grouped into a cohort of similar speakers. For each cohort, one or more complementary speaker models are generated by merging speaker models outside that cohort. When training data from a new speaker to be enrolled is received over a new channel, the speaker model tree as well as the complementary models are updated. Consequently, adaptation to data from new environments is possible by incorporating such data into the verification model whenever it is encountered.

Disclosed is method for screening an audio for fraud detection, the method comprising: providing a User Interface (UI) control capable of: a) receiving an audio; b) comparing the audio with a list of fraud audios; c) assigning a risk score to the audio based on the comparison with a potentially matching fraud audio of the list of fraud audios; and d) displaying an audio interface on a display screen, wherein the audio interface is capable of playing the audio along with the potentially matching fraud audio, and wherein the display screen further displays metadata for each of the audio and the potentially matching fraud audio thereon, wherein the metadata includes location and incident data of each of the audio and the potentially matching fraud audio.

A system authenticates an electronic transaction between a first user-operated device and a computer. The computer is configured to conduct electronic transactions. The system includes a voice browser and a session correlator. The voice browser is configured to receive and process user-spoken information from a second user-operated device, where the voice browser is programmed to compare a user-spoken transaction identifier to a computer generated transaction identifier, and to compare a user-spoken verification identifier to a voice print of the user. The session correlator is coupled to the voice browser and is configured to transmit an authentication message to the computer if the user-spoken transaction identifier matches the computer transaction identifier, and if the user-spoken verification identifier matches the voice print.

A speaker verification method is proposed that first builds a general model of user utterances using a set of general training speech data. The user also trains the system by providing a training utterance, such as a passphrase or other spoken utterance. Then in a test phase, the user provides a test utterance which includes some background noise as well as a test voice sample. The background noise is used to bring the condition of the training data closer to that of the test voice sample by modifying the training data and a reduced set of the general data, before creating adapted training and general models. Match scores are generated based on the comparison between the adapted models and the test voice sample, with a final match score calculated based on the difference between the match scores. This final match score gives a measure of the degree of matching between the test voice sample and the training utterance and is based on the degree of matching between the speech characteristics from extracted feature vectors that make up the respective speech signals, and is not a direct comparison of the raw signals themselves. Thus, the method can be used to verify a speaker without necessarily requiring the speaker to provide an identical test phrase to the phrase provided in the training sample.

Disclosed is an automatic voice call connection service method using a personal phone book database constructed through voice recognition, which includes the steps of allocating personal databases corresponding to phone number to each service user; storing name text, speaker verification feature, speaker-dependent voice recognition name feature and called phone number to the personal database; extracting the speaker verification feature when the speaker requires a phone call and then sending the feature to a voice recognition server/database; verifying the speaker with the feature and searching a name text and a called phone number with the speaker-dependent voice recognition name feature; checking the searched name text and sending the text as a voice to the user; and connecting the call through a public switched telephone network (PSTN).