Methods, systems, and apparatus, including computer programs encoded on computer storage media relate to a method for target speaker extraction. A target speaker extraction system receives an audio frame of an audio signal. A multi-speaker detection model analyzes the audio frame to determine whether the audio frame includes only a single-speaker or multiple speakers. When the audio frame includes only a single-speaker, the system inputs the audio frame to a target speaker VAD model to suppress speech in the audio frame from a non-target speaker based on comparing the audio frame to a voiceprint of a target speaker. When the audio frame includes multiple speakers, the system inputs the audio frame to a speech separation model to separate the voice of the target speaker from a voice mixture in the audio frame.

Methods, systems, and apparatus, including computer programs encoded on computer storage media relate to a method for target speaker extraction. A target speaker extraction system receives an audio frame of an audio signal. A multi-speaker detection model analyzes the audio frame to determine whether the audio frame includes only a single-speaker or multiple speakers. When the audio frame includes only a single-speaker, the system inputs the audio frame to a target speaker VAD model to suppress speech in the audio frame from a non-target speaker based on comparing the audio frame to a voiceprint of a target speaker. When the audio frame includes multiple speakers, the system inputs the audio frame to a speech separation model to separate the voice of the target speaker from a voice mixture in the audio frame.

The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.

The present invention is directed to a deep neural network (DNN) having a triplet network architecture, which is suitable to perform speaker recognition. In particular, the DNN includes three feed-forward neural networks, which are trained according to a batch process utilizing a cohort set of negative training samples. After each batch of training samples is processed, the DNN may be trained according to a loss function, e.g., utilizing a cosine measure of similarity between respective samples, along with positive and negative margins, to provide a robust representation of voiceprints.

A voice processing apparatus includes a reception portion, a production portion and a transmission portion. The reception portion receives sound signals. The production portion produces voice data corresponding to a voice of a speaker through extraction of information of a specific frequency band from the sound signals or through removal of information of a frequency band other than the frequency band of the specific frequency band from the sound signals. The transmission portion transmits the voice data.

A voice processing apparatus includes a reception portion, a production portion and a transmission portion. The reception portion receives sound signals. The production portion produces voice data corresponding to a voice of a speaker through extraction of information of a specific frequency band from the sound signals or through removal of information of a frequency band other than the frequency band of the specific frequency band from the sound signals. The transmission portion transmits the voice data.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: selecting a reference phrase; presenting the reference phrase to a user; generating a voice signature the reference phrase when the reference phrase is vocalized by the user; storing the voice signature for reference phrase within a data center monitoring and management console; instructing the user to recite a subset of words from the reference phrase; and, granting access to the data center monitoring and management console when the subset of words match respective voice signatures stored within the data center monitoring and management console.

A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: selecting a reference phrase; presenting the reference phrase to a user; generating a voice signature the reference phrase when the reference phrase is vocalized by the user; storing the voice signature for reference phrase within a data center monitoring and management console; instructing the user to recite a subset of words from the reference phrase; and, granting access to the data center monitoring and management console when the subset of words match respective voice signatures stored within the data center monitoring and management console.

A terminal may include a display that is divided into at least two areas, when a real time broadcasting, where a user of the terminal is a host, starts through a broadcasting channel, and of which one area of the at least two areas is allocated to the host; an input/output interface that receives a voice of the host; a communication interface that receives one item selected of at least one or more items and a certain text from a terminal of a certain guest, of at least one or more guests who entered the broadcasting channel; and a processor that generates a voice message converted from the certain text into the voice of the host or a voice of the certain guest.

A terminal may include a display that is divided into at least two areas, when a real time broadcasting, where a user of the terminal is a host, starts through a broadcasting channel, and of which one area of the at least two areas is allocated to the host; an input/output interface that receives a voice of the host; a communication interface that receives one item selected of at least one or more items and a certain text from a terminal of a certain guest, of at least one or more guests who entered the broadcasting channel; and a processor that generates a voice message converted from the certain text into the voice of the host or a voice of the certain guest.