Abstract: A labeling processing device (100) generates first label information by labeling time information in a forward direction with respect to a plurality of phoneme boundaries set in speech information for learning. The labeling processing device (100) generates second label information by labeling time information in a direction opposite to the forward direction with respect to a plurality of phoneme boundaries set in speech information for learning and inverting the order of the labeled time information. The labeling processing device (100) detects whether phoneme boundaries are appropriate on the basis of a difference between time information on a plurality of phoneme boundaries included in the first label information and time information on a plurality of phoneme boundaries included in the second label information.

Abstract: A generation device (100) extracts a plurality of integrated speech samples by repeatedly executing processing of integrating a plurality of consecutive speech samples included in speech waveform information into one speech sample, and generates a compressed speech sample by compressing the plurality of integrated speech samples extracted. The generation device (100) generates a plurality of new integrated speech samples subsequent to the plurality of integrated speech samples by inputting the compressed speech sample and an acoustic feature value calculated from the speech waveform information to a speech waveform generation model, and repeatedly executes processing of inputting a compressed speech sample obtained by compressing the plurality of new integrated speech samples and the acoustic feature value to the speech waveform generation model, to generate a plurality of new integrated speech samples a plurality of times.

Abstract: An acoustic model learning device is provided for obtaining an acoustic model used to synthesize voice signals with intonation. The device includes a first learning unit that learns the acoustic model to estimate synthetic acoustic feature values using voice and speaker determination models based on acoustic feature values of speakers, language feature values corresponding to the acoustic feature values and speaker data items, a second learning unit that learns the voice determination model to determine whether the synthetic acoustic feature value is a predetermined acoustic feature value or not based on the acoustic feature values and the synthetic acoustic feature values, and a third learning unit that learns the speaker determination model to determine whether the speaker of the synthetic acoustic feature value is a predetermined speaker or not based on the acoustic feature values and the synthetic acoustic feature values.

Abstract: A detection device includes a labeling acoustic feature calculation unit configured to calculate a labeling acoustic feature from voice data, a time information acquisition unit configured to acquire a label with time information corresponding to the voice data from a label with no time information corresponding to the voice data and the labeling acoustic feature through a use of a labeling acoustic model configured to receive, as inputs, a label with no time information and a labeling acoustic feature and output a label with time information, an acoustic feature prediction unit configured to predict an acoustic feature corresponding to the label with time information and acquire a predicted value through a use of an acoustic model configured to receive, as an input, a label with time information and output an acoustic feature, an acoustic feature calculation unit configured to calculate an acoustic feature from the voice data, a difference calculation unit configured to determine an acoustic difference betwe

Abstract: An acoustic model learning device is provided for obtaining an acoustic model used to synthesize voice signals with intonation. The device includes a first learning unit that learns the acoustic model to estimate synthetic acoustic feature values using voice and speaker determination models based on acoustic feature values of speakers, language feature values corresponding to the acoustic feature values and speaker data items, a second learning unit that learns the voice determination model to determine whether the synthetic acoustic feature value is a predetermined acoustic feature value or not based on the acoustic feature values and the synthetic acoustic feature values, and a third learning unit that learns the speaker determination model to determine whether the speaker of the synthetic acoustic feature value is a predetermined speaker or not based on the acoustic feature values and the synthetic acoustic feature values.