Abstract: A quantitative prediction method for gas content of deep marine shale includes: obtaining raw data of known wells; establishing relationship formulas between pore specific surface areas and adsorbed gas contents of a known well in an area as an adsorbed gas content quantitative prediction model; establishing relationship formulas between pore volumes and free gas contents of the known well as a free gas content quantitative prediction model; summing the adsorbed gas contents and corresponding free gas contents to obtain total gas contents; calculating adsorbed gas contents, free gas contents and total gas contents of the known wells; drawing a predicted adsorbed gas content contour map, a predicted free gas content contour map and a predicted total gas content contour map; and reading an adsorbed gas content, a free gas content and a total gas content of an unknown well in the area from the above contour maps.

Abstract: A rapid monitoring and discrimination method for drought conditions in summer maize based on chlorophyll content includes: 1) Obtaining multi-spectral imagery through UAV multi-payload low-altitude remote sensing technology and measuring chlorophyll content on the ground. Additionally, calculating vegetation indices including Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Renormalized Difference Vegetation Index (RENDVI). 2) Selecting vegetation indices and constructing regression equations with measured chlorophyll content during different growth stages. The regression equation with the highest correlation for each growth stage is chosen as the optimal model equation for that particular stage. 3) Using the optimal model equations to retrieve chlorophyll content for each period and determining thresholds for chlorophyll content across different drought levels through calibration.

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.