In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

A direct speech-to-speech translation (S2ST) model includes an encoder configured to receive an input speech representation that to an utterance spoken by a source speaker in a first language and encode the input speech representation into a hidden feature representation. The S2ST model also includes an attention module configured to generate a context vector that attends to the hidden representation encoded by the encoder. The S2ST model also includes a decoder configured to receive the context vector generated by the attention module and predict a phoneme representation that corresponds to a translation of the utterance in a second different language. The S2ST model also includes a synthesizer configured to receive the context vector and the phoneme representation and generate a translated synthesized speech representation that corresponds to a translation of the utterance spoken in the different second language.

A two way communication assembly includes a display housing that is positionable on a support surface such that the display housing is visible to a pair of users. A pair of displays and a pair of qwerty keyboards is each integrated into opposite sides of the display housing. A translation unit is integrated into the display housing and the translation unit stores a database comprising a plurality of languages spoken around the world. The translation unit translates language between the qwerty keyboards to facilitate a patient who speaks a first language to communicate with a caregiver that speaks a second language. In this way the translation unit facilitates the caregiver to communicate with the patient.

A two way communication assembly includes a display housing that is positionable on a support surface such that the display housing is visible to a pair of users. A pair of displays and a pair of qwerty keyboards is each integrated into opposite sides of the display housing. A translation unit is integrated into the display housing and the translation unit stores a database comprising a plurality of languages spoken around the world. The translation unit translates language between the qwerty keyboards to facilitate a patient who speaks a first language to communicate with a caregiver that speaks a second language. In this way the translation unit facilitates the caregiver to communicate with the patient.

The disclosure refers to electronic apparatuses and controlling methods thereof. In an embodiment, an electronic apparatus includes an input interface, an output interface, and a processor that is communicatively coupled to the input interface and the output interface. The processor is configured to control the input interface to receive conversation data including one or more texts and one or more images. The processor is further configured to extract a first text and an image from the conversation data. The processor is further configured to identify a meaning of the conversation data based on at least one of the first text and the image. The processor is further configured to control the output interface to output the meaning of the conversation data.

The disclosure refers to electronic apparatuses and controlling methods thereof. In an embodiment, an electronic apparatus includes an input interface, an output interface, and a processor that is communicatively coupled to the input interface and the output interface. The processor is configured to control the input interface to receive conversation data including one or more texts and one or more images. The processor is further configured to extract a first text and an image from the conversation data. The processor is further configured to identify a meaning of the conversation data based on at least one of the first text and the image. The processor is further configured to control the output interface to output the meaning of the conversation data.

A discourse-level text translation method and device, the method comprising: acquiring a text to be translated, the text to be translated being a unit text in a discourse-level text to be translated (S101); acquiring an associated text of the text to be translated, the associated text including at least one of a preceding source text, a following source text, and a preceding target text (S102); and translating, according to the associated text, the text to be translated (S103).

Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

Computer-implemented methods for speech synthesis are provided. A speech synthesizer may be trained to generate synthesized audio data that corresponds to words uttered by a source speaker according to speech characteristics of a target speaker. The speech synthesizer may be trained by time-stamped phoneme sequences, pitch contour data and speaker identification data. The speech synthesizer may include a voice modeling neural network and a conditioning neural network.

Computer-implemented methods for speech synthesis are provided. A speech synthesizer may be trained to generate synthesized audio data that corresponds to words uttered by a source speaker according to speech characteristics of a target speaker. The speech synthesizer may be trained by time-stamped phoneme sequences, pitch contour data and speaker identification data. The speech synthesizer may include a voice modeling neural network and a conditioning neural network.