Abstract: Methods and systems for outputting an engagement communicative strategy are described. In an example, a processor may train a communicative model using a patient authored text corpus. The processor may generate at least one patient profile based on the patient authored text corpus and patient authored health data. The processor may construct a knowledge based system based on the communicative model and the at least one patient profile. The processor may input a patient profile of the entity, an engagement degree, and an engagement score to the knowledge based system. The processor may execute the knowledge based system to determine the engagement communicative strategy, and may output the engagement communicative strategy. The engagement communicative strategy may specify a communication scheme to communicate with the entity.

Abstract: Training a machine by a machine learning technique for recognizing speech utterance to determine language fluency level of a user. Native speaker recorded data and language specific dictionary of heteronyms may be retrieved. The native speaker recorded data may be parsed and the heteronyms from the native speaker recorded data may be isolated. Linguistic features from the native speaker recorded data including at least linguistic features associated with the heteronyms may be extracted, and a language dependent machine learning model is generated based on the linguistic features.

Abstract: Methods and systems for outputting an engagement communicative strategy are described. In an example, a processor may train a communicative model using a patient authored text corpus. The processor may generate at least one patient profile based on the patient authored text corpus and patient authored health data. The processor may construct a knowledge based system based on the communicative model and the at least one patient profile. The processor may input a patient profile of the entity, an engagement degree, and an engagement score to the knowledge based system. The processor may execute the knowledge based system to determine the engagement communicative strategy, and may output the engagement communicative strategy. The engagement communicative strategy may specify a communication scheme to communicate with the entity.

Abstract: Training a machine by a machine learning technique for recognizing speech utterance to determine language fluency level of a user. Native speaker recorded data and language specific dictionary of heteronyms may be retrieved. The native speaker recorded data may be parsed and the heteronyms from the native speaker recorded data may be isolated. Linguistic features from the native speaker recorded data including at least linguistic features associated with the heteronyms may be extracted, and a language dependent machine learning model is generated based on the linguistic features.

Abstract: Training a machine by a machine learning technique for recognizing speech utterance to determine language fluency level of a user. Native speaker recorded data and language specific dictionary of heteronyms may be retrieved. The native speaker recorded data may be parsed and the heteronyms from the native speaker recorded data may be isolated. Linguistic features from the native speaker recorded data including at least linguistic features associated with the heteronyms may be extracted, and a language dependent machine learning model is generated based on the linguistic features.

Abstract: Training a machine by a machine learning technique for recognizing speech utterance to determine language fluency level of a user. Native speaker recorded data and language specific dictionary of heteronyms may be retrieved. The native speaker recorded data may be parsed and the heteronyms from the native speaker recorded data may be isolated. Linguistic features from the native speaker recorded data including at least linguistic features associated with the heteronyms may be extracted, and a language dependent machine learning model is generated based on the linguistic features.