Abstract: A dust-proof, light-transmitting member disposed on a light-receiving surface of an imaging device, comprising a light-transmitting substrate, at least a light-entering surface of which is provided with a dust-proof coating having fine roughness on the surface.

Abstract: A protective cover for a display panel, comprising a transparent substrate, and an anti-reflection, transparent medium layer comprising hollow, transparent, fine particles dispersed in a transparent resin matrix and sealed without gap between the a transparent substrate and an image-displaying area of the display panel, the refractive index N1 of the anti-reflection, transparent medium layer and the refractive index Ns of the transparent substrate meeting the following relation (1): No<N1<Ns2??(1), wherein No is the refractive index of air.

Abstract: A silica aerogel coating is produced by reacting a wet gel obtained by the hydrolysis and polymerization of alkoxysilane with an organic-modifying agent to form organically modified silica, dispersing the organically modified silica by an ultrasonic treatment to form an organically modified silica dispersion, adding an ultraviolet-curable resin and a photo-polymerization initiator to the dispersion to prepare a coating liquid, applying the coating liquid to a substrate, and then irradiating ultraviolet rays to the coated layer.

Abstract: A method for producing a silica aerogel coating comprising the steps of organically modifying a wet gel formed by the hydrolysis and polymerization of an alkoxysilane having an ultraviolet-polymerizable unsaturated group, and subjecting a layer of the resultant organically modified silica to ultraviolet irradiation and baking.

Abstract: An anti-reflection coating having a luminous reflectance of 2% or less to light at an incident angle of 0-60° and excellent scratch resistance, which comprises a dense layer and a porous silica aerogel layer formed in this order on a substrate such that a refractive index successively decreases from said substrate to said porous layer.

Abstract: To provide a speech recognition apparatus which appropriately performs speech recognition by generating, in real time, language models adapted to a new topic even in the case where topics are changed.

Abstract: The speech recognition apparatus (1) is equipped with the garbage acoustic model storage unit (110) storing the garbage acoustic model which learned the collection of the unnecessary words; the feature value calculation unit (101) which calculates the feature parameter necessary for recognition by acoustically analyzing the unidentified input speech including the non-language speech per frame which is a unit for speech analysis; the garbage acoustic score calculation unit (111) which calculates the garbage acoustic score by comparing the feature parameter and the garbage acoustic model; the garbage acoustic score correction unit (113) which corrects the garbage acoustic score calculated by the garbage acoustic score calculation unit (111) so as to raise it in the frame where the non-language speech is inputted; and the recognition result output unit (105) which outputs, as the recognition result of the unidentified input speech, the word string with the highest cumulative score of the language score, the word

Abstract: In an acoustic model producing apparatus, a plurality of noise samples are categorized into clusters so that a number of the clusters is smaller than that of noise samples. A noise sample is selected in each of the clusters to set the selected noise samples to second noise samples for training. On the other hand, untrained acoustic models are stored on a storage unit so that the untrained acoustic models are trained by using the second noise samples for training, thereby producing trained acoustic models for speech recognition so as to produce a trained acoustic model for speech recognition.

Abstract: In an acoustic model producing apparatus, a plurality of noise samples are categorized into clusters so that a number of the clusters is smaller than that of noise samples. A noise sample is selected in each of the clusters to set the selected noise samples to second noise samples for training. On the other hand, untrained acoustic models are stored on a storage unit so that the untrained acoustic models are trained by using the second noise samples for training, thereby producing trained acoustic models for speech recognition so as to produce a trained acoustic model for speech recognition.

Abstract: An inter-frame similarity between an input voice and a standard patterned word is calculated for each of frames and for each of standard patterned words, and a posterior probability similarity is produced by subtracting a constant value from each of the inter-frame similarities. The constant value is determined by analyzing voice data obtained from specified persons to set the posterior probability similarities to positive values when a word existing in the input voice matches with the standard patterned word and to set the posterior probability similarities to negative values when a word existing in the input voice does not match with the standard patterned word. Thereafter, an accumulated similarity having an accumulated value obtained by accumulating values of the posterior probability similarities according to a continuous dynamic programming matching operation for the frames of the input voice is calculated for each of the standard patterned words.