Lg Electronics Inc.

Yongchul Park

Jonghoon Chae

Jaemin Kim

Jungkyu Choi

Sungmin Han

Siyoung Yang

Juyeong Jang

Minook Kim

Sangki Kim

Wonho Shin

Jong Hoon Chae

Seokbok Jang

Junghee Kim

Sang Ki Kim

Joonyup Lee

Jongse Park

Hwansik Yun

Hyunjin An

Sung Rock Lee

An artificial intelligence device includes a memory and a processor. The memory is configured to store audio data having a predetermined speech style. The processor is configured to generate a condition vector relating to a condition for determining the speech style of the audio data, reduce a dimension of the condition vector to a predetermined reduction dimension, acquire a sparse code vector based on a dictionary vector acquired through sparse dictionary coding with respect to the condition vector having the predetermined reduction dimension, and change a vector element value included in the sparse code vector.

Artificial intelligence device and method for generating speech having a different speech style

Disclosed is a device for controlling a plurality of voice recognition devices for determining and selecting a first voice recognition device that a user wants to use based on a point in time when the voice of the user is spoken or a place where the user spoke the voice. The device for controlling a plurality of voice recognition devices according to the present disclosure may be associated with an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, etc.

Device, system and method for controlling a plurality of voice recognition devices

A method and apparatus for controlling a device according to an embodiment of the present disclosure may be based on a speech feature of a user reflecting the Lombard effect so as to operate a device located far away from the user, among a plurality of electronic devices. As such, even when the user calls a device located far away from the user without any separate context information, speech recognition neural networks and weight calculation neural networks may be selected and used to operate the device located far away from the user, and reception of a speech signal of the user calling a device located far away from the user may be performed in an Internet of Things (IoT) environment using a 5G network.

Method and apparatus for controlling device located a distance away from a user

According to one embodiment, an apparatus for processing a voice signal includes a display configured to display an image of a user or a character corresponding to the user, a microphone, a speaker configured to output a voice signal of the user, a memory configured to store a trained voice age conversion model, and a processor configured to, based on changing an age of the user or the character displayed on the display, control the display such that the display displays the user or the character corresponding to the changed age. The processor is further configured to determine a first age that is a current age of the user or the character based on the voice signal of the user inputted through the microphone. Accordingly, convenience of a user may be enhanced.

Apparatus for voice-age adjusting an input voice signal according to a desired age

A method includes generating first synthesized speech by using text and a first emotion vector configured for the text, extracting a second emotion vector included in the first synthesized speech, determining whether correction of the second emotion information vector is needed by comparing a loss value calculated by using the first emotion information vector and the second emotion information vector with a preconfigured threshold, re-performing speech synthesis by using a third emotion information vector generated by correcting the second emotion information vector, and outputting the generated synthesized speech, thereby configuring emotion information of speech in a more effective manner. A speech synthesis apparatus may be associated with an artificial intelligence module, drone (unmanned aerial vehicle, UAV), robot, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G services, and the like.

Method for synthesized speech generation using emotion information correction and apparatus

A voice recognition device and a method for learning voice data using the same are disclosed. The voice recognition device combines feature information for various speakers with a text-to-speech function to generate voice data recognized by a voice recognition unit, and can improve voice recognition efficiency by allowing the voice recognition unit itself to learn various voice data. The voice recognition device can be associated with an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

Voice recognition device and method for learning voice data

An electronic device is disclosed. The electronic device includes a memory and a processor. The electronic device may execute an artificial intelligence (AI) algorithm and/or a machine learning algorithm, and perform communications with other electronic devices in a 5G communication network. Accordingly, user convenience can be significantly improved.

Electronic apparatus and method for operating the same

Disclosed is gathering a user's speech samples. According to an embodiment of the disclosure, a method of gathering learning samples may gather a speaker's speech data obtained while talking on a mobile terminal and text data generated from the speech data and gather training data for generating a speech synthesis model. According to the disclosure, the method of gathering learning samples may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Gathering user's speech samples

Disclosed are an emotion classification information-based text-to-speech (TTS) method and device. The emotion classification information-based TTS method according to an embodiment of the present invention may, when emotion classification information is set in a received message, transmit metadata corresponding to the set emotion classification information to a speech synthesis engine and, when no emotion classification information is set in the received message, generate new emotion classification information through semantic analysis and context analysis of sentences in the received message and transmit the metadata to the speech synthesis engine. The speech synthesis engine may perform speech synthesis by carrying emotion classification information based on the transmitted metadata.

Emotion classification information-based text-to-speech (TTS) method and apparatus

Disclosed is speech synthesis in a noisy environment. According to an embodiment of the disclosure, a method of speech synthesis may generate a Lombard effect-applied synthesized speech using a feature vector generated from an utterance feature. According to the disclosure, the speech synthesis method and device may be related to artificial intelligence (AI) modules, unmanned aerial vehicles (UAVs), robots, augmented reality (AR) devices, virtual reality (VR) devices, and 5G service-related devices.

Speech synthesis in noisy environment

Disclosed are a speech recognition verification device and a speech recognition verification method, which verify speech recognition results by executing artificial intelligence (AI) algorithms and/or machine learning algorithms in a 5G environment connected for Internet-of-Things. According to an embodiment, the speech recognition verification method includes converting a verification target text item to a verification target spoken utterance by applying a preset utterance condition, analyzing the verification target spoken utterance and outputting a recognition result text item corresponding to an analysis result, and verifying speech recognition performance through comparison between the verification target text item and the recognition result text item. According to the present disclosure, the speech recognition result may be verified objectively by using a spoken utterance generated with random text and various utterance conditions as input of speech recognition.

Apparatus and method for inspecting speech recognition

An artificial intelligence device includes a speaker, a microphone configured to receive a user's speech, and one or more controllers configured to extract an utterance feature of the received speech, determine a user type corresponding to the extracted utterance feature, map a speech agent associated with the determined user type, and output an audio response through the speaker using the mapped speech agent.

Artificial intelligence device

A voice synthesis device which includes a database configured to store a voice and a text corresponding to the voice and a processor configured to extract characteristic information and a tone of a first-language voice stored in the database, classify an utterance style of an utterer on basis of the extracted characteristic information, generate utterer analysis information including the utterance style and the tone, translate a text corresponding to the first-language voice into a second language, and synthesize the text, translated into the second language, in a second-language voice by using the utterer analysis information.

Voice synthesis device

An apparatus that includes a microphone and a processor. The processor is configured to receive, via the microphone, audio comprising voice of a person, and determine whether the received audio is an actual voice or a synthesized voice. The apparatus also provides a first notification indicating that the received audio is the actual voice when the received audio is the actual voice, and provides a second notification indicating that the received audio is the synthesized voice when the received audio is the synthesized voice.

Voice interpretation device

Discussed is an artificial intelligence (AI)-based voice sampling apparatus for providing a speech style, including a rhyme encoder configured to receive a user's voice, extract a voice sample, and analyze a vocal feature included in the voice sample, a text encoder configured to receive text for reflecting the vocal feature, a processor configured to classify the vocal feature of the voice sample input to the rhyme encoder according to a label, extract an embedding vector representing the vocal feature from the label, and generate a speech style from the embedding vector and apply the generated speech style to the text, and a rhyme decoder configured to output synthesized voice data in which the speech style is applied to the text by the processor.

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Seoul, South Korea

Yongchul Park