Patent application number | Description | Published |
20130191129 | Information Processing Device, Large Vocabulary Continuous Speech Recognition Method, and Program - System and method for performing speech recognition using acoustic invariant structure for large vocabulary continuous speech. An information processing device receives sound as input and performs speech recognition. The information processing device includes: a speech recognition processing unit for outputting a speech recognition score, a structure score calculation unit for calculation of a structure score that is a score that, with respect for each hypothesis concerning all phoneme pairs comprising the hypothesis, is found by applying phoneme pair-by-pair weighting to phoneme pair inter-distribution distance likelihood and then performing summation, and a ranking unit for ranking the multiple hypotheses based on a sum value of speech recognition score and structure score. | 07-25-2013 |
20140358533 | PRONUNCIATION ACCURACY IN SPEECH RECOGNITION - A reading accuracy-improving system includes: a reading conversion unit for retrieving a plurality of candidate word strings from speech recognition results to determine the reading of each candidate word string; a reading score calculating unit for determining the speech recognition score for each of one or more candidate word strings with the same reading to determine a reading score; and a candidate word string selection unit for selecting a candidate to output from the plurality of candidate word strings on the basis of the reading score and speech recognition score corresponding to each candidate word string. | 12-04-2014 |
20150051899 | CORRECTING N-GRAM PROBABILITIES BY PAGE VIEW INFORMATION - Methods and a system for calculating N-gram probabilities in a language model. A method includes counting N-grams in each page of a plurality of pages or in each document of a plurality of documents to obtain respective N-gram counts therefor. The method further includes applying weights to the respective N-gram counts based on at least one of view counts and rankings to obtain weighted respective N-gram counts. The view counts and the rankings are determined with respect to the plurality of pages or the plurality of documents. The method also includes merging the weighted respective N-gram counts to obtain merged weighted respective N-gram counts for the plurality of pages or the plurality of documents. The method additionally includes calculating a respective probability for each of the N-grams based on the merged weighted respective N-gram counts. | 02-19-2015 |
20150051902 | CORRECTING N-GRAM PROBABILITIES BY PAGE VIEW INFORMATION - Methods and a system for calculating N-gram probabilities in a language model. A method includes counting N-grams in each page of a plurality of pages or in each document of a plurality of documents to obtain respective N-gram counts therefor. The method further includes applying weights to the respective N-gram counts based on at least one of view counts and rankings to obtain weighted respective N-gram counts. The view counts and the rankings are determined with respect to the plurality of pages or the plurality of documents. The method also includes merging the weighted respective N-gram counts to obtain merged weighted respective N-gram counts for the plurality of pages or the plurality of documents. The method additionally includes calculating a respective probability for each of the N-grams based on the merged weighted respective N-gram counts. | 02-19-2015 |
20150279353 | UNSUPERVISED TRAINING METHOD, TRAINING APPARATUS, AND TRAINING PROGRAM FOR N-GRAM LANGUAGE MODEL - A computer-based, unsupervised training method for an N-gram language model includes reading, by a computer, recognition results obtained as a result of speech recognition of speech data; acquiring, by the computer, a reliability for each of the read recognition results; referring, by the computer, to the recognition result and the acquired reliability to select an N-gram entry; and training, by the computer, the N-gram language model about selected one of more of the N-gram entries using all recognition results. | 10-01-2015 |
20150294665 | UNSUPERVISED TRAINING METHOD, TRAINING APPARATUS, AND TRAINING PROGRAM FOR N-GRAM LANGUAGE MODEL - A computer-based, unsupervised training method for an N-gram language model includes reading, by a computer, recognition results obtained as a result of speech recognition of speech data; acquiring, by the computer, a reliability for each of the read recognition results; referring, by the computer, to the recognition result and the acquired reliability to select an N-gram entry; and training, by the computer, the N-gram language model about selected one of more of the N-gram entries using all recognition results. | 10-15-2015 |
20150302848 | SPEECH RETRIEVAL METHOD, SPEECH RETRIEVAL APPARATUS, AND PROGRAM FOR SPEECH RETRIEVAL APPARATUS - A method for speech retrieval includes acquiring a keyword designated by a character string, and a phoneme string or a syllable string, detecting one or more coinciding segments by comparing a character string that is a recognition result of word speech recognition with words as recognition units performed for speech data to be retrieved and the character string of the keyword, calculating an evaluation value of each of the one or more segments by using the phoneme string or the syllable string of the keyword to evaluate a phoneme string or a syllable string that is recognized in each of the detected one or more segments and that is a recognition result of phoneme speech recognition with phonemes or syllables as recognition units performed for the speech data, and outputting a segment in which the calculated evaluation value exceeds a predetermined threshold. | 10-22-2015 |
20150310860 | SPEECH RETRIEVAL METHOD, SPEECH RETRIEVAL APPARATUS, AND PROGRAM FOR SPEECH RETRIEVAL APPARATUS - A method for speech retrieval includes acquiring a keyword designated by a character string, and a phoneme string or a syllable string, detecting one or more coinciding segments by comparing a character string that is a recognition result of word speech recognition with words as recognition units performed for speech data to be retrieved and the character string of the keyword, calculating an evaluation value of each of the one or more segments by using the phoneme string or the syllable string of the keyword to evaluate a phoneme string or a syllable string that is recognized in each of the detected one or more segments and that is a recognition result of phoneme speech recognition with phonemes or syllables as recognition units performed for the speech data, and outputting a segment in which the calculated evaluation value exceeds a predetermined threshold. | 10-29-2015 |