An automatic interpretation method performed by a correspondent terminal communicating with an utterer terminal includes receiving, by a communication unit, voice feature information about an utterer and an automatic translation result, obtained by automatically translating a voice uttered in a source language by the utterer in a target language, from the utterer terminal and performing, by a sound synthesizer, voice synthesis on the basis of the automatic translation result and the voice feature information to output a personalized synthesis voice as an automatic interpretation result. The voice feature information about the utterer includes a hidden variable including a first additional voice result and a voice feature parameter and a second additional voice feature, which are extracted from a voice of the utterer.

An automatic interpretation method performed by a correspondent terminal communicating with an utterer terminal includes receiving, by a communication unit, voice feature information about an utterer and an automatic translation result, obtained by automatically translating a voice uttered in a source language by the utterer in a target language, from the utterer terminal and performing, by a sound synthesizer, voice synthesis on the basis of the automatic translation result and the voice feature information to output a personalized synthesis voice as an automatic interpretation result. The voice feature information about the utterer includes a hidden variable including a first additional voice result and a voice feature parameter and a second additional voice feature, which are extracted from a voice of the utterer.

An enunciation system (ES) enables users to gain acquaintance, understanding, and mastery of the relationship between letters and sounds in the context of an alphabetic writing system. The ES enables the user to experience the action of sounding out a word, before their own phonics knowledge enables them to sound out the word independently; its continuous, unbroken speech output or input avoids the common confusions that ensue from analyzing words by breaking them up into discrete sounds; its user-controlled pacing allows the user to slow down enunciation at specific points of difficulty within the word; its real-time touch control allows the written word to be played like a musical instrument, with expressive and aesthetic possibilities; and its highlighting of the letter cluster that is responsible for the recognized phoneme enunciated by the user as it occurs allows the user to more easily associated the letters with the sounds.

A parameter included in a fundamental frequency pattern of a voice can be estimated from the fundamental frequency pattern with high accuracy and the fundamental frequency pattern of the voice can be reconstructed from the parameter included in the fundamental frequency pattern. A learning unit 30 learns a deep generation model including an encoder which regards a parameter included in a fundamental frequency pattern in a voice signal as a latent variable of the deep generation model and estimates the latent variable from the fundamental frequency pattern in the voice signal on the basis of parallel data of the fundamental frequency pattern in the voice signal and the parameter included in the fundamental frequency pattern in the voice signal, and a decoder which reconstructs the fundamental frequency pattern in the voice signal from the latent variable.

A parameter included in a fundamental frequency pattern of a voice can be estimated from the fundamental frequency pattern with high accuracy and the fundamental frequency pattern of the voice can be reconstructed from the parameter included in the fundamental frequency pattern. A learning unit 30 learns a deep generation model including an encoder which regards a parameter included in a fundamental frequency pattern in a voice signal as a latent variable of the deep generation model and estimates the latent variable from the fundamental frequency pattern in the voice signal on the basis of parallel data of the fundamental frequency pattern in the voice signal and the parameter included in the fundamental frequency pattern in the voice signal, and a decoder which reconstructs the fundamental frequency pattern in the voice signal from the latent variable.

An urgency level estimation technique of estimating an urgency level of a speaker for free uttered speech, which does not require a specific word, is provided. An urgency level estimation apparatus includes a feature amount extracting part configured to extract a feature amount of an utterance from uttered speech, and an urgency level estimating part configured to estimate an urgency level of a speaker of the uttered speech from the feature amount based on a relationship between a feature amount extracted from uttered speech and an urgency level of a speaker of the uttered speech, the relationship being determined in advance, and the feature amount includes at least one of a feature indicating speaking speed of the uttered speech, a feature indicating voice pitch of the uttered speech and a feature indicating a power level of the uttered speech.

A voice control processing method and apparatus, where the method includes enabling, by a terminal in a data service disabled state, a data service after the terminal receives a voice instruction using a first application, where the first application is an application program used for voice control in the terminal, prohibiting, by the terminal, another application other than the first application in the terminal from using the data service, and controlling, by the terminal, the first application to execute the voice instruction using the data service, after the terminal enables the data service. The terminal in a data service disabled state receives the voice instruction. Then, the terminal enables the data service and prohibits another application from using the data service.

A reference acoustic input is processed into a quantization representation such that the quantization representation comprises acoustic components determined from the reference acoustic input, wherein the acoustic components comprise amplitude, rhythm, and pitch frequency of the reference acoustic input. A visual representation is generated that simultaneously depicts the acoustic components comprising amplitude, rhythm, and pitch frequency of the reference acoustic input. A user spoken input may be received and similarly processed and displayed.

A reference acoustic input is processed into a quantization representation such that the quantization representation comprises acoustic components determined from the reference acoustic input, wherein the acoustic components comprise amplitude, rhythm, and pitch frequency of the reference acoustic input. A visual representation is generated that simultaneously depicts the acoustic components comprising amplitude, rhythm, and pitch frequency of the reference acoustic input. A user spoken input may be received and similarly processed and displayed.

A method and system for converting a source voice to a target voice is disclosed. The method comprises: recording source voice data and target voice data; extracting spectral envelope features from the source voice data and target voice data; time-aligning pairs of frames based on the extracted spectral envelope features; converting each pair of frames into a frequency domain; generating a plurality of frequency-warping factor candidates, wherein each of the plurality of frequency-warping factor candidates is associated with one of the pairs of frames; generating a single global frequency-warping factor based on the candidates; acquiring source speech; converting the source speech to target speech based on the global frequency-warping factor; generating a waveform comprising the target speech; and playing the waveform comprising the target speech to a user.