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US 12,592,218 B2Granted
Communication device, communication method, and non-transitory storage medium
AssigneeJVCKENWOOD Corporation
InventorsHideo Tsuru, Motomu Takada, Hideya Tsujii
Life of this patent
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Abstract
A communication device includes: a vibration transmitting unit configured to transmit an input vibration wave to a first portion of a subject; a vibration receiving unit configured to receive, at a second portion of the subject, an output vibration wave generated based on the input vibration wave propagated through at least a part of the subject; and a speech recognition device configured to recognize a phoneme which is uttered by the subject based on a difference wave between the input vibration wave and the output vibration wave, wherein the first portion and the second portion are arranged on right and left of the subject.
Claims3
A communication device comprising:
- a vibration transmitting unit configured to transmit an input vibration wave to a first portion of a subject;
- a vibration receiving unit configured to receive, at a second portion of the subject, an output vibration wave generated based on the input vibration wave propagated through at least a part of the subject;
- and
- a speech recognition device configured to recognize a phoneme uttered by the subject based on a difference wave between the input vibration wave and the output vibration wave, wherein
- the first portion and the second portion are arranged on right and left of the subject at bilaterally symmetrical positions with respect to a center line of a body of the subject,
- the first portion is designated to an articular disc of a temporomandibular joint of a right side, and
- the second portion is designated to an articular disc of a temporomandibular joint of a left side,
- wherein the speech recognition device includes:
- a difference-wave generating unit configured to calculate the difference wave;
- an orthogonal transformer configured to perform orthogonal transformation on the difference wave to calculate a frequency spectrum of the difference wave;
- a feature-amount extracting unit configured to extract a feature-amount of the difference wave;
- and
- a processor configured to identify a phoneme uttered by the subject based on the feature-amount extracted by the feature-amount extracting unit, and
- further comprising a learned-model storage unit configured to:
- acquire teacher data that indicates a relationship between the phoneme uttered by the subject and the frequency spectrum of the difference wave;
- generate, by performing machine learning based on the teacher data, a learned model into which the frequency spectrum is input and from which a phoneme which is uttered by the subject is output;
- and
- store the learned model, wherein
- the processor is further configured to identify the phoneme by inputting the feature-amount extracted by the feature-amount extracting unit into the learned model.
16 more elements
A communication method comprising:
- transmitting an input vibration wave to a first portion of a subject;
- receiving, at a second portion of the subject, an output vibration wave generated based on the input vibration wave propagated through at least a part of the subject;
- and
- recognizing a phoneme uttered by the subject based on a difference wave between the input vibration wave and the output vibration wave, wherein
- the first portion and the second portion are arranged on right and left of the subject at bilaterally symmetrical positions with respect to a center line of a body of the subject,
- the first portion is designated to an articular disc of a temporomandibular joint of a right side, and
- the second portion is designated to an articular disc of a temporomandibular joint of a left side,
- and the communication method further comprising:
- calculating the difference wave;
- performing orthogonal transformation on the difference wave to calculate a frequency spectrum of the difference wave;
- extracting a feature-amount of the difference wave;
- identifying a phoneme uttered by the subject based on the feature-amount extracted;
- acquiring teacher data that indicates a relationship between the phoneme uttered by the subject and the frequency spectrum of the difference wave;
- generating, by performing machine learning based on the teacher data, a learned model into which the frequency spectrum is input and from which a phoneme which is uttered by the subject is output;
- storing the learned model;
- and
- identifying the phoneme by inputting the feature-amount extracted into the learned model.
14 more elements
A non-transitory storage medium that stores a computer-program for causing a computer to execute a communication method including:
- transmitting an input vibration wave to a first portion of a subject;
- receiving, at a second portion of the subject, an output vibration wave generated based on the input vibration wave propagated through at least a part of the subject;
- and
- recognizing a phoneme uttered by the subject based on a difference wave between the input vibration wave and the output vibration wave, wherein
- the first portion and the second portion are arranged on a right side and a left side of the subject at bilaterally symmetrical positions with respect to a center line of a body of the subject,
- the first portion is designated to an articular disc of a temporomandibular joint of a right side, and
- the second portion is designated to an articular disc of a temporomandibular joint of a left side,
- and the communication method further including:
- calculating the difference wave;
- performing orthogonal transformation on the difference wave to calculate a frequency spectrum of the difference wave;
- extracting a feature-amount of the difference wave;
- identifying a phoneme uttered by the subject based on the feature-amount extracted;
- acquiring teacher data that indicates a relationship between the phoneme uttered by the subject and the frequency spectrum of the difference wave;
- generating, by performing machine learning based on the teacher data, a learned model into which the frequency spectrum is input and from which a phoneme which is uttered by the subject is output;
- storing the learned model;
- and
- identifying the phoneme by inputting the feature-amount extracted into the learned model.
14 more elements
Dependencies are inferred from the claim text.