Abstract: An electronic apparatus, based on a text sentence being input, obtains prosody information of the text sentence, segments the text sentence into a plurality of sentence elements, obtains a speech in which prosody information is reflected to each of the plurality of sentence elements in parallel by inputting the plurality of sentence elements and the prosody information of the text sentence to a text to speech (TTS) module, and merges the speech for the plurality of sentence elements that are obtained in parallel to output speech for the text sentence.

Abstract: A controlling method of a wearable electronic apparatus includes: receiving, by an IMU sensor, a bone conduction signal corresponding to vibration in the user's face, while the wearable electronic apparatus is operated in an ANC mode; identifying a presence or an absence of the user's voice based on the bone conduction signal; based on the identifying the presence of the user's voice, controlling an operation mode of the wearable electronic apparatus to be a different operation mode from the ANC mode; while the wearable electronic apparatus is operated in the different operation mode, identifying presence or absence of the user's voice based on the bone conduction signal; and based on the absence of the user's voice being identified for a predetermined time while the wearable electronic apparatus is operated in the different operation mode, controlling the different operation mode to return to the ANC mode.

Abstract: A method for controlling an electronic device includes obtaining a text, obtaining, by inputting the text into a first neural network model, acoustic feature information corresponding to the text and alignment information in which each frame of the acoustic feature information is matched with each phoneme included in the text, identifying an utterance speed of the acoustic feature information based on the alignment information, identifying a reference utterance speed for each phoneme included in the acoustic feature information based on the text and the acoustic feature information, obtaining utterance speed adjustment information based on the utterance speed of the acoustic feature information and the reference utterance speed for each phoneme, and obtaining, based on the utterance speed adjustment information, speech data corresponding to the text by inputting the acoustic feature information into a second neural network model.

Abstract: An electronic apparatus, a terminal apparatus, and a controlling method thereof. The electronic apparatus includes an input interface; and a processor including a prosody module configured to extract an acoustic feature and a vocoder module configured to generate a speech waveform, wherein the processor is configured to: receive a text input using the input interface; identify a first acoustic feature from the text input using the prosody module, wherein the first acoustic feature corresponds to a first sampling rate; generate a modified acoustic feature corresponding to a modified sampling rate different from the first sampling rate, based on the identified first acoustic feature; and generate a plurality of vocoder learning models by training the vocoder module based on the first acoustic feature and the modified acoustic feature.

Abstract: A system for synthesising expressive speech includes: an interface configured to receive an input text for conversion to speech; a memory; and at least one processor coupled to the memory. The processor is configured to generate, using an expressivity characterisation module, a plurality of expression vectors, wherein each expression vector is a representation of prosodic information in a reference audio style file, and synthesise expressive speech from the input text, using an expressive acoustic model comprising a deep convolutional neural network that is conditioned by at least one of the plurality of expression vectors.

Abstract: An electronic apparatus and a controlling method thereof are provided. The electronic apparatus includes a microphone; a memory configured to store a text-to-speech (TTS) model and a plurality of evaluation texts; and a processor configured to: obtain a first reference vector of a user speech spoken by a user based the user speech being received through the microphone, generate a plurality of candidate reference vectors based on the first reference vector, obtain a plurality of synthesized sounds by inputting the plurality of candidate reference vectors and the plurality of evaluation texts to the TTS model, identify at least one synthesized sound of the plurality of synthesized sounds based on a similarity between characteristics of the plurality of synthesized sounds and the user speech, and store a second reference vector of the at least one synthesized sound in the memory as a reference vector corresponding to the user for the TTS model.

Abstract: An electronic apparatus which acquires input data to be input into a TTS module for outputting a voice through the TTS module, acquires a voice signal corresponding to the input data through the TTS module, detects an error in the acquired voice signal based on the input data, corrects the input data based on the detection result, and acquires a corrected voice signal corresponding to the corrected input data through the TTS module.

Abstract: An electronic apparatus which acquires input data to be input into a TTS module for outputting a voice through the TTS module, acquires a voice signal corresponding to the input data through the TTS module, detects an error in the acquired voice signal based on the input data, corrects the input data based on the detection result, and acquires a corrected voice signal corresponding to the corrected input data through the TTS module.

Abstract: A method for controlling an electronic device includes obtaining a text, obtaining, by inputting the text into a first neural network model, acoustic feature information corresponding to the text and alignment information in which each frame of the acoustic feature information is matched with each phoneme included in the text, identifying an utterance speed of the acoustic feature information based on the alignment information, identifying a reference utterance speed for each phoneme included in the acoustic feature information based on the text and the acoustic feature information, obtaining utterance speed adjustment information based on the utterance speed of the acoustic feature information and the reference utterance speed for each phoneme, and obtaining, based on the utterance speed adjustment information, speech data corresponding to the text by inputting the acoustic feature information into a second neural network model.

Abstract: The electronic device may include a communication interface; a memory configured to store a first neural network model; and a processor configured to: receive, from an external electronic device via the communication interface, compressed information related to an acoustic feature obtained based on a text; decompress the compressed information to obtain decompressed information; and obtain sound information corresponding to the text by inputting the decompressed information into the first neural network model. The first neural network model may be obtained by training a relationship between a plurality of sample acoustic features and a plurality of sample sounds corresponding to the plurality of sample acoustic features.

Abstract: The disclosure relates to an electronic device and a control method thereof. The electronic device includes a memory, and a processor configured to: obtain first feature data for estimating a waveform by inputting acoustic data of a first quality to a first encoder model; and obtain waveform data of a second quality that is a higher quality than the first quality by inputting the first feature data to a decoder model to.

Abstract: A controlling method of a wearable electronic apparatus includes: receiving, by an IMU sensor, a bone conduction signal corresponding to vibration in the user's face, while the wearable electronic apparatus is operated in an ANC mode; identifying a presence or an absence of the user's voice based on the bone conduction signal, based on the identifying the presence of the user's voice, controlling an operation mode of the wearable electronic apparatus to be a different operation mode from the ANC mode; while the wearable electronic apparatus is operated in the different operation mode, identifying presence or absence of the user's voice based on the bone conduction signal, and based on the absence of the user's voice being identified for a predetermined time while the wearable electronic apparatus is operated in the different operation mode, controlling the different operation mode to return to the ANC mode.

Abstract: A speech synthesis method performed by an electronic apparatus to synthesize speech from text and includes: obtaining text input to the electronic apparatus; obtaining a text representation by encoding the text using a text encoder of the electronic apparatus; obtaining an audio representation of a first audio frame set from an audio encoder of the electronic apparatus, based on the text representation; obtaining an audio representation of a second audio frame set based on the text representation and the audio representation of the first audio frame set; obtaining an audio feature of the second audio frame set by decoding the audio representation of the second audio frame set; and synthesizing speech based on an audio feature of the first audio frame set and the audio feature of the second audio frame set.

Abstract: An electronic apparatus, based on a text sentence being input, obtains prosody information of the text sentence, segments the text sentence into a plurality of sentence elements, obtains a speech in which prosody information is reflected to each of the plurality of sentence elements in parallel by inputting the plurality of sentence elements and the prosody information of the text sentence to a text to speech (TTS) module, and merges the speech for the plurality of sentence elements that are obtained in parallel to output speech for the text sentence.

Abstract: An electronic device and a controlling method of the electronic device are provided. The electronic device acquires text to respond on a received user's speech, acquires a plurality of pieces of parameter information for determining a style of an output speech corresponding to the text based on information on a type of a plurality of text-to-speech (TTS) databases and the received user's speech, identifies a TTS database corresponding to the plurality of pieces of parameter information among the plurality of TTS databases, identifies a weight set corresponding to the plurality of pieces of parameter information among a plurality of weight sets acquired through a trained artificial intelligence model, adjusts information on the output speech stored in the TTS database based on the weight set, synthesizes the output speech based on the adjusted information on the output speech, and outputs the output speech corresponding to the text.

Abstract: An electronic apparatus and a controlling method thereof are provided. The electronic apparatus includes a microphone; a memory configured to store a text-to-speech (TTS) model and a plurality of evaluation texts; and a processor configured to: obtain a first reference vector of a user speech spoken by a user based the user speech being received through the microphone, generate a plurality of candidate reference vectors based on the first reference vector, obtain a plurality of synthesized sounds by inputting the plurality of candidate reference vectors and the plurality of evaluation texts to the TTS model, identify at least one synthesized sound of the plurality of synthesized sounds based on a similarity between characteristics of the plurality of synthesized sounds and the user speech, and store a second reference vector of the at least one synthesized sound in the memory as a reference vector corresponding to the user for the TTS model.

Abstract: An electronic apparatus, based on a text sentence being input, obtains prosody information of the text sentence, segments the text sentence into a plurality of sentence elements, obtains a speech in which prosody information is reflected to each of the plurality of sentence elements in parallel by inputting the plurality of sentence elements and the prosody information of the text sentence to a text to speech (TTS) module, and merges the speech for the plurality of sentence elements that are obtained in parallel to output speech for the text sentence.

Abstract: A method, performed by an electronic device, of generating a speech signal corresponding to at least one text is provided. The method includes obtaining feature information with respect to a first sample included in the speech signal, based on the at least one text, obtaining condition information related to a condition under which a bunching operation, in which one or more sample values included in the speech signal are obtained, is performed, based on the feature information, configuring one or more bunching blocks for performing the bunching operation, based on the condition information, obtaining the one or more sample values based on the feature information with respect to the first sample by using the one or more bunching blocks, and generating the speech signal based on the obtained one or more sample values.

Abstract: A system for synthesising expressive speech includes: an interface configured to receive an input text for conversion to speech; a memory; and at least one processor coupled to the memory. The processor is configured to generate, using an expressivity characterisation module, a plurality of expression vectors, wherein each expression vector is a representation of prosodic information in a reference audio style file, and synthesise expressive speech from the input text, using an expressive acoustic model comprising a deep convolutional neural network that is conditioned by at least one of the plurality of expression vectors.

Abstract: A speech synthesis method performed by an electronic apparatus to synthesize speech from text and includes: obtaining text input to the electronic apparatus; obtaining a text representation by encoding the text using a text encoder of the electronic apparatus; obtaining an audio representation of a first audio frame set from an audio encoder of the electronic apparatus, based on the text representation; obtaining an audio representation of a second audio frame set based on the text representation and the audio representation of the first audio frame set; obtaining an audio feature of the second audio frame set by decoding the audio representation of the second audio frame set; and synthesizing speech based on an audio feature of the first audio frame set and the audio feature of the second audio frame set.