Abstract: 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.

Abstract: A system and method extract off-topic parts from a conversation. The system includes a first corpus including documents of a plurality of fields; a second corpus including only documents of a field to which the conversation belongs; a determination means for determination as a lower limit subject word a word for which IDF value for the first corpus and IDF value for the second corpus are each below a first certain threshold value; a score calculation part for calculation as a score a TF-IDF value for each word included in the second corpus; a clipping part, for sequential cutting out of intervals from text data that are contents of the conversation; and an extraction part for extraction as an off-topic part an interval where average value of the score of words included in the clipped interval is larger than a second certain threshold value.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus, method and program for dividing a conversational dialog into utterance. The apparatus includes: a computer processor; a word database for storing spellings and pronunciations of words; a grammar database for storing syntactic rules on words; a pause detecting section which detects a pause location in a channel making a main speech among conversational dialogs inputted in at least two channels; an acknowledgement detecting section which detects an acknowledgement location in a channel not making the main speech; a boundary-candidate extracting section which extracts boundary candidates in the main speech, by extracting pauses existing within a predetermined range before and after a base point that is the acknowledgement location; and a recognizing unit which outputs a word string of the main speech segmented by one of the extracted boundary candidates after dividing the segmented speech into optimal utterance in reference to the word database and grammar database.

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: A dialog processing system which includes a target expression data extraction unit for extracting a plurality of target expression data each including a pattern matching portion which matches an utterance pattern, which are inputted by an utterance pattern input unit and is an utterance structure derived from contents of field-independent general conversations, among a plurality of utterance data which are inputted by an utterance data input unit and obtained by converting contents of a plurality of conversations in one field; a feature extraction unit for retrieving the pattern matching portions, respectively, from the plurality of target expression data extracted, and then for extracting feature quantity common to the plurality of pattern matching portions; and a mandatory data extraction unit for extracting mandatory data in the one field included in the plurality of utterance data by use of the feature quantities extracted.

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: Exemplary embodiments provide for determining a sequence of words in a TTS system. An input text is analyzed using two models, a word n-gram model and an accent class n-gram model. A list of all possible words for each word in the input is generated for each model. Each word in each list for each model is given a score based on the probability that the word is the correct word in the sequence, based on the particular model. The two lists are combined and the two scores are combined for each word. A set of sequences of words are generated. Each sequence of words comprises a unique combination of an attribute and associated word for each word in the input. The combined score of each of word in the sequence of words is combined. A sequence of words having the highest score is selected and presented to a user.

Abstract: A system, method, and computer readable article of manufacture for extracting a specific situation in a conversation. The system includes: an acquisition unit for acquiring speech voice data of speakers in the conversation; a specific expression detection unit for detecting the speech voice data of a specific expression from speech voice data of a specific speaker in the conversation; and a specific situation extraction unit for extracting, from the speech voice data of the speakers in the conversation, a portion of the speech voice data that forms a speech pattern that includes the speech voice data of the specific expression detected by the specific expression detection unit.

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: A dialog processing system which includes a target expression data extraction unit for extracting a plurality of target expression data each including a pattern matching portion which matches an utterance pattern, which are inputted by an utterance pattern input unit and is an utterance structure derived from contents of field-independent general conversations, among a plurality of utterance data which are inputted by an utterance data input unit and obtained by converting contents of a plurality of conversations in one field; a feature extraction unit for retrieving the pattern matching portions, respectively, from the plurality of target expression data extracted, and then for extracting feature quantity common to the plurality of pattern matching portions; and a mandatory data extraction unit for extracting mandatory data in the one field included in the plurality of utterance data by use of the feature quantities extracted.

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: 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: An apparatus, method and program for dividing a conversational dialog into utterance. The apparatus includes: a computer processor; a word database for storing spellings and pronunciations of words; a grammar database for storing syntactic rules on words; a pause detecting section which detects a pause location in a channel making a main speech among conversational dialogs inputted in at least two channels; an acknowledgement detecting section which detects an acknowledgement location in a channel not making the main speech; a boundary-candidate extracting section which extracts boundary candidates in the main speech, by extracting pauses existing within a predetermined range before and after a base point that is the acknowledgement location; and a recognizing unit which outputs a word string of the main speech segmented by one of the extracted boundary candidates after dividing the segmented speech into optimal utterance in reference to the word database and grammar database.

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 speech recognition apparatus can include a transformation processor configured to transform at least one phoneme sequence included in speech into at least one word sequence, and to provide the word sequence with an appearance probability indicating that the phoneme sequence originally represented the word sequence. A renewal processor can renew the appearance probability based on a renewed numerical value indicated by language models corresponding to the word sequence provided by the transformation processor. A recognition processor can select one of the word sequences for which the renewed appearance probability is the highest to indicate that the phoneme sequence originally represented the selected word sequence. The renewal processor can calculate the renewed numerical value using a first language model prepared for expressions unique to spontaneous speech, and a second language model different from the first which employs the renewed numerical value to renew the appearance probability.