Samsung Electronics Co., Ltd.

Mohammad Mahdi Moazzami

Anil Sunder Yadav

Pu Song

Vijendra R Apsingekar

Asif Ali

Zheng Zhou

Alireza Dirafzoon

Allan Jay Schwade

Abdul Rafay Khalid

A method includes performing, using at least one processor, feature extraction of input audio data to identify extracted features associated with the input audio data. The method also includes detecting, using the at least one processor, a language associated with the input audio data by processing the extracted features using a plurality of language models, where each language model is associated with a different language. The method further includes directing, using the at least one processor, the input audio data to one of a plurality of automatic speech recognition (ASR) models based on the language associated with the input audio data.

System and method for multi-spoken language detection

A method includes providing input data to a plurality of base models to generate a plurality of intermediate outputs. The base models are non-linear in that different base models are specialized differently such that the different base models are complementary to one another. Each of the base models is generated using a different base classification algorithm in a multi-layered machine learning system. The method also includes processing the intermediate outputs using a fusion model to generate a final output associated with the input data. The fusion model is generated using a meta classification algorithm in the multi-layered machine learning system. The method may also include training the classification algorithms, where training data used by each of at least one of the base classification algorithms is selected based on an uncertainty associated with at least one other of the base classification algorithms.

Multi-layered machine learning system to support ensemble learning

An apparatus for contextual execution comprises a processor, and a memory containing instructions, which when executed by the processor, cause the apparatus to receive, from a user terminal, a control input associated with an intent, obtain location data associated with a location of the user terminal, and determine a scored set of execution options associated with the control input. Further, the instructions, when executed by the processor cause the apparatus to obtain a contextual label associated with the location data, the label determined based on the application of one or more adapted pretrained deep learning models to the location data.

System and method for deep labeling

A method, an electronic device, and computer readable medium is provided. The method includes identifying a frequency of each word that is present within a set of words. The method also includes deriving relatedness values for pairs of words. Each pair of words includes a first word and a second word in the set of words. Each relatedness value corresponds to a respective one of the pairs of words. Each relatedness value is based on the identified frequencies that the first word and the second word of the respective pair of words are present within the set of words. The method further includes generating a matrix representing the relatedness values. The method additionally includes generating a language model that represents relationships between the set of words included in the matrix.

System and method supporting context-specific language model

A method includes performing, using at least one processor, feature extraction of input audio data to identify extracted features associated with the input audio data. The method also includes detecting, using the at least one processor, a language associated with each of multiple portions of the input audio data by processing the extracted features using a plurality of language models, where each language model is associated with a different language. In addition, the method includes directing, using the at least one processor, each portion of the input audio data to one of a plurality of automatic speech recognition (ASR) models based on the language associated with the portion of the input audio data.

A method, an electronic device, and computer readable medium is provided. The method includes identifying a set of observable features associated with one or more users. The method also includes generating latent features from the set of observable features. The method additionally includes sorting the latent features into one or more clusters. Each of the one or more clusters represents verbal utterances of a group of users that share a portion of the latent features. The method further includes generating a language model that corresponds to a specific cluster of the one or more clusters. The language model represents a probability ranking of the verbal utterances that are associated with the group of users of the specific cluster.

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San Jose, CA, United States of America

Mohammad Mahdi Moazzami