Abstract: A method for evaluating synthesized speech, including receiving a speech sample in a first language; and determining a rating of the speech sample based on an encoding of the speech sample by an artificial intelligence encoding model, the rating of the speech sample corresponding to a naturalness of the speech sample, wherein the encoding of the speech sample is based on a first training stage of the encoding model using a first set of training data and a second training stage of the encoding model using a second set of training data, the first set of training data includes unlabeled speech audio, unlabeled text, and paired speech audio and text data in the first language and at least one additional language, and the second set of training data includes rated speech audio.

Abstract: A method for evaluating synthesized speech, including receiving a speech sample in a first language; and determining a rating of the speech sample based on an encoding of the speech sample by an artificial intelligence encoding model, the rating of the speech sample corresponding to a naturalness of the speech sample, wherein the encoding of the speech sample is based on a first training stage of the encoding model using a first set of training data and a second training stage of the encoding model using a second set of training data, the first set of training data includes unlabeled speech audio, unlabeled text, and paired speech audio and text data in the first language and at least one additional language, and the second set of training data includes rated speech audio.

Abstract: A method for identifying codemixed text includes receiving codemixed text and segmenting the codemixed text into a plurality of tokens. Each token includes at least one character and is delineated from any adjacent tokens by a space. For each token of the codemixed text, the method also includes extracting features from the token and predicting a probability distribution over possible languages for the token using a language identifier model configured to receive the extracted features from the token as feature inputs. The method also includes assigning a language to each token of the codemixed text by executing a greedy search on the probability distribution over the possible languages predicted for each respective token.

Abstract: A method for identifying codemixed text includes receiving codemixed text and segmenting the codemixed text into a plurality of tokens. Each token includes at least one character and is delineated from any adjacent tokens by a space. For each token of the codemixed text, the method also includes extracting features from the token and predicting a probability distribution over possible languages for the token using a language identifier model configured to receive the extracted features from the token as feature inputs. The method also includes assigning a language to each token of the codemixed text by executing a greedy search on the probability distribution over the possible languages predicted for each respective token.