Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.