Methods of authenticating a user or speaker are provided. These methods include obtaining an input speech signal and user credentials identifying the user or speaker. The input speech signal includes a single-channel signal or a multi-channel speech signal. The methods further include extracting a speech voiceprint from the input speech signal, and retrieving a reference voiceprint associated to the user credentials. The methods still further include determining a voiceprint correspondence between the speech voiceprint and the reference voiceprint, and authenticating the user or speaker depending on said voiceprint correspondence. The methods yet further include updating the reference voiceprint depending on the speech voiceprint corresponding to the authenticated user or speaker. Computer programs, systems and computing systems are also provided which are suitable for performing said methods of authenticating a user or speaker.

Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (ASR) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (GMMs) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (ASR) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (GMMs) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining a media file including a speech by one or more speaker. The language of the speech is identified and biographic data of a speaker of the speech is generated by analyzing semantics and vocal characteristics of the speech. The speaker is diarized and confidence in a resulting speaker label is evaluated against a threshold. The speaker label is adjusted with the language of the speech and biographic data of the speaker and produced as speaker metadata of the media file.

An electronic device is disclosed. The electronic device comprises: a voice input unit; a storage unit for storing a first text according to a first transcript format and at least one second text obtained by transcribing the first text in a second transcript format; and a processor for, when a voice text converted from a user voice input through the voice input unit corresponds to a preset instruction, executing a function according to the preset instruction. The processor executes a function according to a preset instruction when the preset instruction includes a first text and a voice text is a text in which the first text of the preset instruction has been transcribed into a second text of a second transcript format.

An electronic device is disclosed. The electronic device comprises: a voice input unit; a storage unit for storing a first text according to a first transcript format and at least one second text obtained by transcribing the first text in a second transcript format; and a processor for, when a voice text converted from a user voice input through the voice input unit corresponds to a preset instruction, executing a function according to the preset instruction. The processor executes a function according to a preset instruction when the preset instruction includes a first text and a voice text is a text in which the first text of the preset instruction has been transcribed into a second text of a second transcript format.

A voice operation apparatus and a control method thereof that can further improve accuracy of talker identification are provided. Provided is a voice operation apparatus including a talker identification unit that identifies a user as a talker of a voice operation based on voice information and a voice quality model of a user registered in advance, and a voice operation recognition unit that performs voice recognition on the voice information and generates voice operation information, wherein the talker identification unit identifies a talker by using, as auxiliary information, at least one of the voice operation information, position information on a voice operation apparatus, direction information on a talker, distance information on a talker, and time information.

Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (ASR) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (GMMs) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

Features are disclosed for automatically identifying a speaker. Artifacts of automatic speech recognition (ASR) and/or other automatically determined information may be processed against individual user profiles or models. Scores may be determined reflecting the likelihood that individual users made an utterance. The scores can be based on, e.g., individual components of Gaussian mixture models (GMMs) that score best for frames of audio data of an utterance. A user associated with the highest likelihood score for a particular utterance can be identified as the speaker of the utterance. Information regarding the identified user can be provided to components of a spoken language processing system, separate applications, etc.

A speaker recognition device includes a memory, and a processor. The memory stores enrolled key phrase data corresponding to utterances of a key phrase by enrolled users,and text-dependent and text-independent acoustic speaker models of the enrolled users. The processor is operatively connected to the memory, and executes instructions to authenticate a speaker as an enrolled user, which includes detecting input key phrase data corresponding to a key phrase uttered by the speaker, computing text-dependent and text-independent scores for the speaker using speech models of the enrolled user, computing a confidence score, and authenticating or rejecting the speaker as the enrolled user based on whether the confidence score indicates that the input key phrase data corresponds to the speech from the enrolled user.