Sharp Kabushiki Kaisha Corporation

Kouichi Yamaguchi

Kenji Sakamoto

Yoshiji Fujimoto

Toshio Akabane

Takayuki Hayashi

Function distinguishing marks are efficiently displayed which correspond to a plurality of functions assigned to a single operation key, respectively. A mobile phone () changes in accordance with the number of functions assigned to a single operation key the number of the function distinguishing marks to be displayed which indicate functions.

Mobile terminal, display method, display mode determining program, and computer-readable storage medium

A voice recognition apparatus capable of recognizing any word utterance by using a neural network, the apparatus includes a unit for inputting an input utterance and for outputting compressed feature variables of the input utterance a unit using a neural network and connected to the input unit for receiving the compressed feature variables output from the input unit and for outputting a value corresponding to a similarity between the input utterance and words to be recognized. The neural network unit has a first unit for outputting a value which corresponds to a similarity in partial phoneme series of a specific word among vocabularies to be recognized with respect to the input utterance. The neural network also has a second unit connected to the first unit for receiving all of the values output from the first unit and for outputting a value corresponding to a similarity in the specific word with respect to the input utterance. And the neural network also has a third unit connected to the second unit for receiving all of the values output from the second unit and for outputting a value corresponding to a classification of voice recognition in which the input utterance belongs.

Speech recognition system with neural network

A first voice recognition apparatus includes a device for analyzing frequencies of the input voice and a device coupled to the analyzing unit for determining vowel zones and consonant zones of the analyzed input voice. The apparatus further includes a device for determining positions of articulation of an input voice determined from the vowel zones by calculating from frequency components of the input voice in accordance with a predetermined algorithm based on frequency components of monophthongs having known phonation contents and positions of articulation. A second voice recognition apparatus includes a device for analyzing frequencies of the input voice so as to derive acoustic parameters from the input voice. A pattern converting unit is coupled to the analyzing unit and uses a neural network for converting the acoustic parameters to articulartory vectors. The neural network is capable of learning, by the error back propagation method using target data produced by a predetermined sequence based on the acoustic parameters, to create rules for converting the acoustic parameters of the input voice to articulatory vectors having at least two vector elements. A recognizing unit is coupled to the pattern converting unit for recognizing the input voice by comparing a feature pattern of the analyzed input voice having the articulatory vector with reference feature patterns in a predetermined sequence. A storage unit is coupled to the recognizing unit for storing the reference feature patterns having the articulatory vectors created by the pattern converting unit.

Growing community of inventors

Tenri, Japan

Kouichi Yamaguchi