Neosapience, Inc.

Younggun Lee

Taesu Kim

Suwon Shon

A method for generating synthetic speech for text through a user interface is provided. The method may include receiving one or more sentences, determining a speech style characteristic for the received one or more sentences, and outputting a synthetic speech for the one or more sentences that reflects the determined speech style characteristic. The one or more sentences and the determined speech style characteristic may be inputted to an artificial neural network text-to-speech synthesis model and the synthetic speech may be generated based on the speech data outputted from the artificial neural network text-to-speech synthesis model.

Method and system for generating synthetic speech for text through user interface

A speech translation method using a multilingual text-to-speech synthesis model includes receiving input speech data of the first language and an articulatory feature of a speaker regarding the first language, converting the input speech data of the first language into a text of the first language, converting the text of the first language into a text of the second language, and generating output speech data for the text of the second language that simulates the speaker's speech by inputting the text of the second language and the articulatory feature of the speaker to a single artificial neural network text-to-speech synthesis model.

Translation method and system using multilingual text-to-speech synthesis model

The present disclosure relates to a text-to-speech synthesis method using machine learning based on a sequential prosody feature. The text-to-speech synthesis method includes receiving input text, receiving a sequential prosody feature, and generating output speech data for the input text reflecting the received sequential prosody feature by inputting the input text and the received sequential prosody feature to an artificial neural network text-to-speech synthesis model.

Method, device, and computer readable storage medium for text-to-speech synthesis using machine learning on basis of sequential prosody feature

A speech translation method using a multilingual text-to-speech synthesis model includes acquiring a single artificial neural network text-to-speech synthesis model having acquired learning based on a learning text of a first language and learning speech data of the first language corresponding to the learning text of the first language, and a learning text of a second language and learning speech data of the second language corresponding to the learning text of the second language, receiving input speech data of the first language and an articulatory feature of a speaker regarding the first language, converting the input speech data of the first language into a text of the first language, converting the text of the first language into a text of the second language, and generating output speech data for the text of the second language that simulates the speaker's speech.

Speech translation method and system using multilingual text-to-speech synthesis model

A multilingual text-to-speech synthesis method and system are disclosed. The method includes receiving an articulatory feature of a speaker regarding a first language, receiving an input text of a second language, and generating output speech data for the input text of the second language that simulates the speaker's speech by inputting the input text of the second language and the articulatory feature of the speaker regarding the first language to a single artificial neural network multilingual text-to-speech synthesis model. The single artificial neural network multilingual text-to-speech synthesis model is generated by learning similarity information between phonemes of the first language and phonemes of the second language based on a first learning data of the first language and a second learning data of the second language.

Multilingual text-to-speech synthesis

A method for searching content having same voice as a voice of a target speaker from among a plurality of contents includes extracting a feature vector corresponding to the voice of the target speaker, selecting any subset of speakers from a training dataset repeatedly by a predetermined number of times, generating linear discriminant analysis (LDA) transformation matrices using each of the selected any subsets of speakers repeatedly by a predetermined number of times, projecting the extracted speaker feature vector to the selected corresponding subsets of speakers using each of the generated LDA transformation matrices, assigning a value corresponding to nearby speaker class among corresponding subsets of speakers, to each of projection regions of the extracted speaker feature vector, generating a hash value corresponding to the extracted feature vector based on the assigned values, and searching content having a similar hash value to the generated hash value among the contents.

Method for searching for contents having same voice as voice of target speaker, and apparatus for executing same

A text-to-speech synthesis method using machine learning, the text-to-speech synthesis method is disclosed. The method includes generating a single artificial neural network text-to-speech synthesis model by performing machine learning based on a plurality of learning texts and speech data corresponding to the plurality of learning texts, receiving an input text, receiving an articulatory feature of a speaker, generating output speech data for the input text reflecting the articulatory feature of the speaker by inputting the articulatory feature of the speaker to the single artificial neural network text-to-speech synthesis model.

Text-to-speech synthesis method and apparatus using machine learning, and computer-readable storage medium

A multilingual text-to-speech synthesis method and system are disclosed. The method includes receiving first learning data including a learning text of a first language and learning speech data of the first language corresponding to the learning text of the first language, receiving second learning data including a learning text of a second language and learning speech data of the second language corresponding to the learning text of the second language, and generating a single artificial neural network text-to-speech synthesis model by learning similarity information between phonemes of the first language and phonemes of the second language based on the first learning data and the second learning data.

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Seoul, South Korea

Younggun Lee