Context-aware query recognition for electronic devices
First Claim
Patent Images
1. An electronic device to execute a context-aware query recognition, the device comprising:
- an input device to receive user speech;
memory to store instructions for performing a context-aware query; and
a controller, coupled to the memory and the input device, the controller being configured to;
generate a speech signal including applying the user speech to a statistically-based speech recognition algorithm,determine, using a first machine learning process employing at least one of a condition random field, hidden Markov model, or neural network, if the speech signal includes words implying an action and whether the words implying the action request the action to be performed,determine, using a second machine learning process employing at least one of a condition random field, Gaussian mixture model, or neural network, the second machine learning process running in parallel with the first machine learning process, if the speech signal includes words associated with an intended recipient of the user speech, andif the speech signal requests the action to be performed and if the intended recipient of the user speech is the electronic device, generate a command for the electronic device to perform the action, wherein the user speech and the speech signal do not include a wake-up phrase.
3 Assignments
0 Petitions
Accused Products
Abstract
A method for context-aware query recognition in an electronic device includes receiving user speech from an input device. A speech signal is generated from the user speech. It is determined if the speech signal includes an action to be performed and if the electronic device is the intended recipient of the user speech. If the recognized speech signal include the action and the intended recipient of the user speech is the electronic device, a command is generated for the electronic device to perform the action.
-
Citations
25 Claims
-
1. An electronic device to execute a context-aware query recognition, the device comprising:
-
an input device to receive user speech; memory to store instructions for performing a context-aware query; and a controller, coupled to the memory and the input device, the controller being configured to; generate a speech signal including applying the user speech to a statistically-based speech recognition algorithm, determine, using a first machine learning process employing at least one of a condition random field, hidden Markov model, or neural network, if the speech signal includes words implying an action and whether the words implying the action request the action to be performed, determine, using a second machine learning process employing at least one of a condition random field, Gaussian mixture model, or neural network, the second machine learning process running in parallel with the first machine learning process, if the speech signal includes words associated with an intended recipient of the user speech, and if the speech signal requests the action to be performed and if the intended recipient of the user speech is the electronic device, generate a command for the electronic device to perform the action, wherein the user speech and the speech signal do not include a wake-up phrase. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A computer-implemented method for context-aware query recognition in an electronic device, the method comprising:
-
receiving user speech from an input device; generating a speech signal including applying the user speech to a statistical-based speech recognition algorithm, wherein the user speech and the speech signal do not include a wake-up phrase; determining, using a first machine learning process employing at least one of a condition random field, hidden Markov model, or neural network, if the speech signal includes words implying an action and determining whether the words implying the action request the action to be performed; determining an intended recipient of the user speech using a second machine learning process employing at least one of a condition random field, Gaussian mixture model, or neural network, the second machine learning process running in parallel with the first machine learning process; and if the speech signal requests the action to be performed and the intended recipient of the user speech is the electronic device, generating a command for the electronic device to perform the action. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
-
-
18. A computer-implemented method for context-aware query recognition in an electronic device, the method comprising:
-
receiving at the electronic device user speech from an input device; generating a speech signal in response to the user speech by applying the user speech to a statistically-based speech recognition algorithm, wherein the user speech and the speech signal do not include a wake-up phrase; analyzing the speech signal using a first machine learning process employing at least one of a condition random field, hidden Markov model, or neural network to determine if the speech signal includes at least one of words or phonemes indicating that the user speech was intended for the electronic device; analyzing the speech signal using a second machine learning process employing at least one of a condition random field, Gaussian mixture model, or neural network, the second machine learning process running in parallel with the first machine learning process to determine if the speech signal includes at least one of words or phonemes implying an action and determining whether the words implying the action request an action; and generating a command for the electronic device based on the requested action. - View Dependent Claims (19, 20)
-
-
21. At least one non-transitory computer-readable medium comprising instructions for executing context-aware query recognition in an electronic device that, when executed by a computer, cause the computer to:
-
receive user speech from an input device; generate a speech signal including applying the user speech to a statistically-based speech recognition algorithm, wherein the user speech and the speech signal do not include a wake-up phrase; determine using a first machine learning process employing at least one of a condition random field, hidden Markov model, or neural network, if the speech signal includes words implying an action and determine using the first machine learning process whether the words implying the action request the action to be performed; determine an intended recipient of the user speech using a second machine learning process employing at least one of a condition random field, Gaussian mixture model, or neural network, the second machine learning process running in parallel with the first machine learning process; and if the speech signal requests the action to be performed and the intended recipient of the user speech is the electronic device, generate a command for the electronic device to perform the action. - View Dependent Claims (22, 23, 24, 25)
-
Specification