A natural language processing method and apparatus are disclosed. A natural language processing method according to an embodiment of the present disclosure includes extracting a phoneme string from a text corpus labeled with recognition information including at least one of one named entity (NE) or speech intention, generating a phoneme-based training data set by labeling the recognition information in the extracted phoneme string, and generating an artificial neural network-based learning model (LM) using the generated training data set. The natural language processing method of the present disclosure may be associated with an artificial intelligence module, a drone (Unmanned Aerial Vehicle, UAV), a robot, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a device associated with 5G services, etc.

Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

Systems, methods, and non-transitory computer-readable media including instructions for detecting and utilizing facial skin micromovements are disclosed. In some non-limiting embodiments, the detection of the facial skin micromovements occurs using a speech detection system that may include a wearable housing, a light source (either a coherent light source or a non-coherent light source), a light detector, and at least one processor. One or more processors may be configured to analyze light reflections received from a facial region to determine the facial skin micromovements, and extract meaning from the determined facial skin micromovements. Examples of meaning that may be extracted from the determined facial skin micromovements may include words spoken by the individual (either silently spoken or vocally spoken), an identification of the individual, an emotional state of the individual, a heart rate of the individual, a respiration rate of the individual, or any other biometric, emotion, or speech-related indicator.

According to an aspect, method for synthesizing multi-speaker speech using an artificial neural network comprises generating and storing a speech learning model for a plurality of users by subjecting a synthetic artificial neural network of a speech synthesis model to learning, based on speech data of the plurality of users, generating speaker vectors for a new user who has not been learned and the plurality of users who have already been learned by using a speaker recognition model, determining a speaker vector having the most similar relationship with the speaker vector of the new user according to preset criteria out of the speaker vectors of the plurality of users who have already been learned, and generating and learning a speaker embedding of the new user by subjecting the synthetic artificial neural network of the speech synthesis model to learning, by using a value of a speaker embedding of a user for the determined speaker vector as an initial value and based on speaker data of the new user.