Mitsubishi Electric Corporation

Mitsubishi Electric Research Laboratories, Inc.

The University of Tokyo

Yohei Okato

Toshiyuki Hanazawa

Jonathan Le Roux

Jun Ishii

Masanobu Osawa

Takefumi Hasegawa

Gordon P Wichern

Michihiro Yamazaki

Keigo Kawashima

Takeyuki Aikawa

Tatsuhiko Saito

Takeshi Oishi

Ryo Aihara

Tomohiro Iwasaki

Naoya Sugitani

Ryoichi Ishikawa

A camera movement amount acquisition unit () acquires as a camera movement amount (), an estimated movement amount of a vehicle () calculated using photographed image data acquired by, a camera () installed on the vehicle (). In synchronization with the acquisition of the camera movement amount () by the camera movement amount acquisition unit (), a LiDAR movement amount acquisition unit () acquires as a LiDAR movement amount (), an estimated movement amount of the vehicle () with higher estimation accuracy than estimation accuracy of the camera movement amount (). A comparison decision unit () compares the camera movement amount () with the LiDAR movement amount (), and decides to use the camera movement amount () for a calibration operation when a difference between the camera movement amount () and the LiDAR movement amount () is less than a threshold value.

Data processing apparatus, data processing method, and computer readable medium

A speech separation device () of a speech separation system includes a feature amount extraction unit () configured to extract time-series data of a speech feature amount of mixed speech, a block division unit () configured to divide the time-series data of the speech feature amount into blocks having a certain time width, a speech separation neural network () configured to create time-series data of a mask of each of a plurality of speakers from the time-series data of the speech feature amount divided into blocks, and a speech restoration unit () configured to restore the speech data of each of the plurality of speakers from the time-series data of the mask and the time-series data of the speech feature amount of the mixed speech.

Voice separation device, voice separation method, voice separation program, and voice separation system

In an acoustic signal separation device (), a determination unit () determines whether or not components from a plurality of sound sources are mixed in each of acoustic signals of respective components regenerated by a signal regeneration unit (), and when it is determined that a plurality of components is mixed, a series of processes by a feature value extraction unit (), a data estimation unit (), a data classification unit (), and a signal regeneration unit () is repeatedly executed until acoustic signals of the components of the respective sound sources are regenerated.

Acoustic signal separation device and acoustic signal separating method

An object is to provide a technique which can provide a highly valid recognition result while preventing unnecessary processing. A voice recognition device includes first to third voice recognition units, and a control unit. When it is decided based on recognition results obtained by the first and second voice recognition units to cause the third voice recognition unit to recognize an input voice, the control unit causes the third voice recognition unit to recognize the input voice by using a dictionary including a candidate character string obtained by at least one of the first and second voice recognition units.

Apparatus and method for decoding to recognize speech using a third speech recognizer based on first and second recognizer results

A detectordetects words acoustically similar to each other from text information, and a selectorselects a synonym from a storagethe synonym corresponding to a word detected by the detectorand being acoustically neither the same as nor similar to any word existing in the text information. A replacerreplaces the word detected by the detectorwith the synonym selected by the selector

Information providing device and information providing method

A search unit 60 calculates a character-string similarity by acquiring expansion source document data partially matching a search query 70 from a character-string search index 40, determines a relational-data expansion similarity by acquiring expansion destination document data defined to have a relationship with the expansion source document data from a relational-data expansion index 50 and correcting the acquired data by the character-string similarity of the expansion destination document data and relation weight data 80, and outputs a search result 90 arranged in an order of the relational-data expansion similarity.

Search device

An index generating unit divides each name data of search target data both into words and into characters, calculates start and end scores showing a start and an end of each of the words and start and end scores showing a start and an end of each of the characters, links them to each entry word which constructs the name data as a list (a name ID, a position, and start and end scores), and stores this list in an index storage unit. A searching unit decomposes an input character string into partial character strings, acquires corresponding candidate entries from the index storage unit, and judges the continuity between candidate entries on the basis of lists to add a comparison score according to the continuity to a candidate entry.

Search device and search program

Voice recognition is realized by a pattern matching with a voice pattern model, and when a large number of paraphrased words are required for one facility, such as a name of a hotel or a tourist facility, the pattern matching needs to be performed with the voice pattern models of all the paraphrased words, resulting in an enormous amount of calculation. Further, it is difficult to generate all the paraphrased words, and a large amount of labor is required. A voice recognition device includes: voice recognition means for applying the voice recognition to an input voice by using a language model and an acoustic model, and outputting a predetermined number of recognition results each including a set of a recognition score and a text representation; and N-best candidate rearrangement means for: comparing the recognition result to a morpheme dictionary held in a morpheme dictionary memory; checking whether a representation of the recognition result can be expressed by any one of combinations of the morphemes of the morpheme dictionaries; correcting the recognition score when the representation can be expressed; and rearranging an order according to the corrected recognition score so as to acquire recognition results.

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Tokyo, Japan

Yohei Okato