Abstract: Systems and methods for smoke detection which utilizes two generally identical light sources and single light detector to compare the ratio of forward and backward scattering smoke particles present in a detection area.

Abstract: A preparation method of (4S,5R)-semiester in which cycloanhydride conducts enantioselective ring-opening with alcohol in the presence of 9-epiquininurea. With this method, (4S,5R)-semiester is prepared at room temperature with high yield and high stereoselectivity.

Abstract: A name entity extraction technique using language models is provided. A general language model is provided for the natural language understanding domain. A language model is also provided for each name entity. The name entity language models are added to the general language model. Each language model is considered a state. Probabilities are applied for each transition within a state and between each state. For each word in an utterance, the name extraction process determines a best current state and a best previous state. When the end of the utterance is reached, the process traces back to find the best path. Each series of words in a state other than the general language model state is identified as a name entity. A technique is provided to iteratively extract names and retrain the general language model until the probabilities do not change.

Abstract: Disclosed is a method of automated speaker identification, comprising receiving a sample speech input signal from a sample handset; deriving a cepstral covariance sample matrix from the first sample speech signal; calculating, with a distance metric, all distances between the sample matrix and one or more cepstral covariance signature matrices; determining if the smallest of the distances is below a predetermined threshold value; and wherein the distance metric is selected from d 5 ? ( S , ? ) = A + 1 H - 2 , d 6 ? ( S , ? ) = ( A + 1 H ) ? ( G + 1 G ) - 4 , d 7 ? ( S , ? ) = A 2 ? ? H ? ( G + 1 G ) - 1 , d 8 ? ( S , ? ) = ( A + 1 H ) ( G + 1 G ) - 1 , d 9 ? ( S , ? ) = A G + G H - 2 , fusion derivatives thereof, and fusion derivatives thereof with ? d 1 ? ( S , ? ) = A H - 1.

Abstract: Disclosed is a method of automated handset identification, comprising receiving a sample speech input signal from a sample handset; deriving a cepstral covariance sample matrix from said first sample speech signal; calculating, with a distance metric, all distances between said sample matrix and one or more cepstral covariance handset matrices, wherein each said handset matrix is derived from a plurality of speech signals taken from different speakers through the same handset; and determining if the smallest of said distances is below a predetermined threshold value.

Abstract: A name entity extraction technique using language models is provided. A general language model is provided for the natural language understanding domain. A language model is also provided for each name entity. The name entity language models are added to the general language model. Each language model is considered a state. Probabilities are applied for each transition within a state and between each state. For each word in an utterance, the name extraction process determines a best current state and a best previous state. When the end of the utterance is reached, the process traces back to find the best path. Each series of words in a state other than the general language model state is identified as a name entity. A technique is provided to iteratively extract names and retrain the general language model until the probabilities do not change.

Abstract: Disclosed is a method of automated speaker identification, comprising receiving a sample speech input signal from a sample handset; deriving a cepstral covariance sample matrix from said first sample speech signal; calculating, with a distance metric, all distances between said sample matrix and one or more cepstral covariance signature matrices; determining if the smallest of said distances is below a predetermined threshold value; and wherein said distance metric is selected from 1 d 5 ⁡ ( S , Σ ) = A + 1 H - 2 , 2 d 6 ⁡ ( S , Σ ) = ( A + 1 H ) ⁢ ( G + 1 G ) - 4 , d 7 ⁡ ( S , Σ ) = A 2 ⁢ H ⁢ ( G + 1 G ) - 1 , d 8 ⁡ ( S , Σ ) = ( A + 1 H ) ( G + 1 G ) - 1 , 3 d 9 ⁡ ( S , Σ ) = A G + G H - 2 ,

Abstract: Disclosed is a method of automated handset identification, comprising receiving a sample speech input signal from a sample handset; deriving a cepstral covariance sample matrix from said first sample speech signal; calculating, with a distance metric, all distances between said sample matrix and one or more cepstral covariance handset matrices, wherein each said handset matrix is derived from a plurality of speech signals taken from different speakers through the same handset; and determining if the smallest of said distances is below a predetermined threshold value.

Abstract: A method for automatically updating stock and mutual fund grammars in a speech recognition system includes the step of automatically updating, on a pre-specified basis, a database having a plurality of entries. Each entry respectively corresponds to a publicly traded stock or a publicly traded fund, and respectively includes at least one name of the publicly traded stock or publicly traded fund, a weight for the at least one name, and baseforms of the at least one name. A grammar file for names in the database is automatically updated. The grammar file includes the names and weights for the names. Preferably, the database and grammar file are updated on a daily basis.