Provided is a system which allows a learner who is a non-native speaker of a given language to intuitively improve pronunciation of the language. A pronunciation conversion apparatus includes a conversion section which converts a first feature value corresponding to a first speech signal obtained when a first speaker who speaks a given language as his/her native language speaks another language such that the first feature value approaches a second feature value corresponding to a second speech signal obtained when a second speaker who speaks the other language as his/her native language speaks the other language, each of the first feature value and the second feature value is a feature value capable of representing a difference in pronunciation, and a speech signal obtained from the first feature value after the conversion is presented to the first speaker.

Provided is a system which allows a learner who is a non-native speaker of a given language to intuitively improve pronunciation of the language. A pronunciation conversion apparatus includes a conversion section which converts a first feature value corresponding to a first speech signal obtained when a first speaker who speaks a given language as his/her native language speaks another language such that the first feature value approaches a second feature value corresponding to a second speech signal obtained when a second speaker who speaks the other language as his/her native language speaks the other language, each of the first feature value and the second feature value is a feature value capable of representing a difference in pronunciation, and a speech signal obtained from the first feature value after the conversion is presented to the first speaker.

An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

This encoding device is able to encode an S signal efficiently in MS prediction encoding. An M signal encoding unit generates first encoding information by encoding a sum signal indicating a sum of a left channel signal and a right channel signal that constitute a stereo signal. An energy difference calculation unit calculates a prediction parameter for predicting a difference signal indicating a difference between the left channel signal and the right channel signal by using a parameter regarding an energy difference between the left channel signal and the right channel signal. An entropy encoding unit generates second encoding information by encoding the prediction parameter.

The present technology pertains to a method and system for assessing risks associated with facilities, based on using natural language processing. For example, a method can include receiving a natural language input comprising at least one raw text document associated with a facility and generating a plurality of segmented sentences from the raw text documents. The plurality of segmented sentences can be provided as inputs to a machine learning model trained to classify an input segmented sentence over a pre-defined lexicon of pharmaceutical terminology. Each segmented sentence can be classified into one or more classes given by the pre-defined lexicon of pharmaceutical terminology. A secondary classification can be performed for each classified segmented sentence to generate a production issue label based on an analysis of the classified segmented sentence. From the secondary classifications for the classified segmented sentences, at least one production category score for the facility can be generated.

The present technology pertains to a method and system for assessing risks associated with facilities, based on using natural language processing. For example, a method can include receiving a natural language input comprising at least one raw text document associated with a facility and generating a plurality of segmented sentences from the raw text documents. The plurality of segmented sentences can be provided as inputs to a machine learning model trained to classify an input segmented sentence over a pre-defined lexicon of pharmaceutical terminology. Each segmented sentence can be classified into one or more classes given by the pre-defined lexicon of pharmaceutical terminology. A secondary classification can be performed for each classified segmented sentence to generate a production issue label based on an analysis of the classified segmented sentence. From the secondary classifications for the classified segmented sentences, at least one production category score for the facility can be generated.

Embodiments of the present disclosure provide methods and systems for processing a speech signal. The method can include: processing the speech signal to generate a plurality of speech frames; generating a first number of acoustic features based on the plurality of speech frames using a frame shift at a given frequency; and generating a second number of posteriori probability vectors based on the first number of acoustic features using an acoustic model, wherein each of the posteriori probability vectors comprises probabilities of the acoustic features corresponding to a plurality of modeling units, respectively.

Embodiments of the present disclosure provide methods and systems for processing a speech signal. The method can include: processing the speech signal to generate a plurality of speech frames; generating a first number of acoustic features based on the plurality of speech frames using a frame shift at a given frequency; and generating a second number of posteriori probability vectors based on the first number of acoustic features using an acoustic model, wherein each of the posteriori probability vectors comprises probabilities of the acoustic features corresponding to a plurality of modeling units, respectively.

An autocorrelation calculation unit 21 calculates an autocorrelation R.sub.O(i) from an input signal. A prediction coefficient calculation unit 23 performs linear prediction analysis by using a modified autocorrelation R′.sub.O(i) obtained by multiplying a coefficient w.sub.O(i) by the autocorrelation R.sub.O(i). It is assumed here, for each order i of some orders i at least, that the coefficient w.sub.O(i) corresponding to the order i is in a monotonically increasing relationship with an increase in a value that is negatively correlated with a fundamental frequency of the input signal of the current frame or a past frame.