Abstract: A connector includes a power terminal, an LA terminal, a second housing portion, a heat storage member, and a cover. The power terminal is to be electrically connected to a mating terminal. The LA terminal is to be electrically connected to a cable. The heat storage member is erected at a connection portion between the power terminal and the LA terminal. The second housing portion includes a second housing space that houses the heat storage member. The cover sheathes an upper end opening of the second housing portion. The cover has a vent hole that penetrates the cover and communicates with the second housing space. The vent film is provided to the vent hole.

Abstract: A document processing method comprising: receiving document information from a terminal; and outputting associated information obtained by associating a subject element and a defining element with each other, and associating the defining element and a limiting element with each other based on the content of the document information, the subject element being a character string representing a subject extracted from a character string of the document information, the defining element being a character string defining an operation and/or a state of the subject, and the limiting element being a character string limiting a content of the defining element.

Abstract: Accuracy of a model extracting a graph structure as an intermediate representation from input data is improved. An encoding unit (100) extracts a feature amount of each of a plurality of vertices included in a graph structure (Tr) from input data (10), and calculates a likelihood that an edge is connected to the vertex. A sampling unit (130) determines the graph structure (Tr) based on a conversion result of a Gumbel-Softmax function for the likelihood. A learning unit (150) optimizes a decoding unit (140) and the encoding unit (100) by back propagation using a loss function including an error (LP) between output data (20) generated from the graph structure (Tr) and correct data.

Abstract: A system that outputs phonemes and accents of texts. The system has a storage section storing a first corpus in which spellings, phonemes, and accents of a text input beforehand are recorded separately for individual segmentations of the words that are contained in the text. A text for which phonemes and accents are to be output is acquired and the first corpus is searched to retrieve at least one set of spellings that match the spellings in the text from among sets of contiguous spellings. Then, the combination of a phoneme and an accent that has a higher probability of occurrence in the first corpus than a predetermined reference probability is selected as the phonemes and accent of the text.

Abstract: An example embodiment of the invention includes a speech recognition processing unit for specifying speech segments for speech data, recognizing a speech in each of the speech segments, and associating a character string of obtained recognition data with the speech data for each speech segment, based on information on a time of the speech, and an output control unit for displaying/outputting the text prepared by sorting the recognition data in each speech segment. Sometimes, the system further includes a text editing unit for editing the prepared text, and a speech correspondence estimation unit for associating a character string in the edited text with the speech data by using a technique of dynamic programming.

Abstract: A word prediction apparatus and method that improves the precision accuracy, and a speech recognition method and an apparatus therefor are provided. For the prediction of a sixth word “?”, a partial analysis tree having a modification relationship with the sixth word is predicted. “sara-ni sho-senkyoku no” has two partial analysis trees, “sara-ni” and “sho-senkyoku no”. It is predicted that “sara-ni” does not have a modification relationship with the sixth word, and that “sho-senkyoku no” does. Then, “donyu”, which is the sixth word from “sho-senkyoku no”, is predicted. In this example, since “sara-ni” is not useful information for the prediction of “donyu”, it is preferable that “donyu” be predicted only by “sho-senkyoku no”.

Abstract: To search out a new word that should be newly registered in a dictionary contained in a segmentation device for segmenting a text into words. This system inputs a training text into the segmentation device to cause the segmentation device to segment the training text into words, and thereby generates a plurality of segmentation candidates in association with certainty factors of the results of the segmentation, the segmentation candidates respectively containing mutually different combinations of words as results of the segmentation of the training text. Then, this system computes a likelihood that the each word is a new word by summing up some of the certainty factors that are respectively associated with some of the plurality of segmentation candidates that contain the each word.

Abstract: Techniques for acquiring, from an input text and an input speech, a set of a character string and a pronunciation thereof which should be recognized as a word. A system according to the present invention: selects, from an input text, plural candidate character strings which are candidates to be recognized as a word; generates plural pronunciation candidates of the selected candidate character strings; generates frequency data by combining data in which the generated pronunciation candidates are respectively associated with the character strings; generates recognition data in which character strings respectively indicating plural words contained in the input speech are associated with pronunciations; and selects and outputs a combination contained in the recognition data, out of combinations each consisting of one of the candidate character strings and one of the pronunciation candidates.

Abstract: A word prediction method and apparatus improves precision and accuracy. For the prediction of a sixth word “?”, a partial analysis tree having a modification relationship with the sixth word is predicted. “sara-ni sho-senkyoku no” has two partial analysis trees, “sara-ni” and “sho-senkyoku no”. It is predicted that “sara-ni” does not have a modification relationship with the sixth word, and that “sho-senkyoku no” does. Then, “donyu”, which is the sixth word from “sho-senkyoku no”, is predicted. In this example, since “sara-ni” is not useful information for the prediction of “donyu”, it is preferable that “donyu” be predicted only by “sho-senkyoku no”.

Abstract: Calculates a word n-gram probability with high accuracy in a situation where a first corpus), which is a relatively small corpus containing manually segmented word information, and a second corpus, which is a relatively large corpus, are given as a training corpus that is storage containing vast quantities of sample sentences. Vocabulary including contextual information is expanded from words occurring in first corpus of relatively small size to words occurring in second corpus of relatively large size by using a word n-gram probability estimated from an unknown word model and the raw corpus. The first corpus (word-segmented) is used for calculating n-grams and the probability that the word boundary between two adjacent characters will be the boundary of two words (segmentation probability). The second corpus (word-unsegmented), in which probabilistic word boundaries are assigned based on information in the first corpus (word-segmented), is used for calculating a word n-grams.

Abstract: A system that outputs phonemes and accents of texts. The system has a storage section storing a first corpus in which spellings, phonemes, and accents of a text input beforehand are recorded separately for individual segmentations of the words that are contained in the text. A text for which phonemes and accents are to be output is acquired and the first corpus is searched to retrieve at least one set of spellings that match the spellings in the text from among sets of contiguous spellings. Then, the combination of a phoneme and an accent that has a higher probability of occurrence in the first corpus than a predetermined reference probability is selected as the phonemes and accent of the text.

Abstract: A word predicting method for use with a voice recognition using a computer includes the steps of specifying a sentence structure of a history up to a word immediately before the word to be predicted, referring to a context tree stored in arboreal context tree storage section having information about possible structures of a sentence and a probability of appearance of words with respect to the structures at nodes, and predicting words based on the context tree and the specified sentence structure of the history.

Abstract: Calculates a word n-gram probability with high accuracy in a situation where a first corpus), which is a relatively small corpus containing manually segmented word information, and a second corpus, which is a relatively large corpus, are given as a training corpus that is storage containing vast quantities of sample sentences. Vocabulary including contextual information is expanded from words occurring in first corpus of relatively small size to words occurring in second corpus of relatively large size by using a word n-gram probability estimated from an unknown word model and the raw corpus. The first corpus (word-segmented) is used for calculating n-grams and the probability that the word boundary between two adjacent characters will be the boundary of two words (segmentation probability). The second corpus (word-unsegmented), in which probabilistic word boundaries are assigned based on information in the first corpus (word-segmented), is used for calculating a word n-grams.

Abstract: A word prediction apparatus and method that improves the precision accuracy, and a speech recognition method and an apparatus therefor are provided. For the prediction of a sixth word “?”, a partial analysis tree having a modification relationship with the sixth word is predicted. “sara-ni sho-senkyoku no” has two partial analysis trees, “sara-ni” and “sho-senkyoku no”. It is predicted that “sara-ni” does not have a modification relationship with the sixth word, and that “sho-senkyoku no” does. Then, “donyu”, which is the sixth word from “sho-senkyoku no”, is predicted. In this example, since “sara-ni” is not useful information for the prediction of “donyu”, it is preferable that “donyu” be predicted only by “sho-senkyoku no”.

Abstract: Techniques for acquiring, from an input text and an input speech, a set of a character string and a pronunciation thereof which should be recognized as a word. A system according to the present invention: selects, from an input text, plural candidate character strings which are candidates to be recognized as a word; generates plural pronunciation candidates of the selected candidate character strings; generates frequency data by combining data in which the generated pronunciation candidates are respectively associated with the character strings; generates recognition data in which character strings respectively indicating plural words contained in the input speech are associated with pronunciations; and selects and outputs a combination contained in the recognition data, out of combinations each consisting of one of the candidate character strings and one of the pronunciation candidates.

Abstract: A word prediction method and apparatus improves precision and accuracy. For the prediction of a sixth word “?”, a partial analysis tree having a modification relationship with the sixth word is predicted. “sara-ni sho-senkyoku no” has two partial analysis trees, “sara-ni” and “sho-senkyoku no”. It is predicted that “sara-ni” does not have a modification relationship with the sixth word, and that “sho-senkyoku no” does. Then, “donyu”, which is the sixth word from “sho-senkyoku no”, is predicted. In this example, since “sara-ni” is not useful information for the prediction of “donyu”, it is preferable that “donyu” be predicted only by “sho-senkyoku no”.

Abstract: To search out a new word that should be newly registered in a dictionary contained in a segmentation device for segmenting a text into words. This system inputs a training text into the segmentation device to cause the segmentation device to segment the training text into words, and thereby generates a plurality of segmentation candidates in association with certainty factors of the results of the segmentation, the segmentation candidates respectively containing mutually different combinations of words as results of the segmentation of the training text. Then, this system computes a likelihood that the each word is a new word by summing up some of the certainty factors that are respectively associated with some of the plurality of segmentation candidates that contain the each word.

Abstract: A word prediction method that improves the precision accuracy, and a speech recognition method and an apparatus therefor are provided. For the prediction of a sixth word “?”, a partial analysis tree having a modification relationship with the sixth word is predicted. “sara-ni sho-senkyoku no” has two partial analysis trees, “sara-ni” and “sho-senkyoku no”. It is predicted that “sara-ni” does not have a modification relationship with the sixth word, and that “sho-senkyoku no” does. Then, “donyu”, which is the sixth word from “sho-senkyoku no”, is predicted. In this example, since “sara-ni” is not useful information for the prediction of “donyu”, it is preferable that “donyu” be predicted only by “sho-senkyoku no”.

Abstract: A system that outputs phonemes and accents of texts. The system has a storage section storing a first corpus in which spellings, phonemes, and accents of a text input beforehand are recorded separately for individual segmentations of the words that are contained in the text. A text for which phonemes and accents are to be output is acquired and the first corpus is searched to retrieve at least one set of spellings that match the spellings in the text from among sets of contiguous spellings. Then, the combination of a phoneme and an accent that has a higher probability of occurrence in the first corpus than a predetermined reference probability is selected as the phonemes and accent of the text.

Abstract: Calculates a word n-gram probability with high accuracy in a situation where a first corpus), which is a relatively small corpus containing manually segmented word information, and a second corpus, which is a relatively large corpus, are given as a training corpus that is storage containing vast quantities of sample sentences. Vocabulary including contextual information is expanded from words occurring in first corpus of relatively small size to words occurring in second corpus of relatively large size by using a word n-gram probability estimated from an unknown word model and the raw corpus. The first corpus (word-segmented) is used for calculating n-grams and the probability that the word boundary between two adjacent characters will be the boundary of two words (segmentation probability). The second corpus (word-unsegmented), in which probabilistic word boundaries are assigned based on information in the first corpus (word-segmented), is used for calculating a word n-grams.