VIRTUAL ASSISTANT WITH ERROR IDENTIFICATION
Virtual assistants provide results in response to user commands and analyze user utterances in response to the result. The analysis can interpret words, recognized from the utterance, as being negative indicators that imply user dissatisfaction. Virtual assistants request follow-up information from users. Analysis also interprets words as indicators of clarification and collect information to add to a knowledgebase. Machine learning algorithms use recognized words to train a behavioral model to improve results. Virtual assistants also infer, from replacement of words in successive commands, that earlier commands had word recognition errors and infer, from addition of words, that earlier commands had interpretation errors. Virtual assistants act locally or as devices in communication with servers.
1 - 13 . (canceled)
14 . A non-transitory computer readable medium comprising code that, if executed by at least one computer processor comprised by a virtual assistant, would cause the virtual assistant to:
receive a first utterance;
recognize a first sequence of words and an alternative sequence of words from the first utterance;
receive a second utterance;
recognize a second sequence of words from the second utterance;
identify that the second sequence of words matches the alternative sequence of words; and
conclude that the first sequence of words had a speech recognition error.
15 - 22 . (canceled)
23 . A non-transitory computer readable medium comprising code that, if executed by at least one computer processor comprised by a virtual assistant, would cause the virtual assistant to:
receive a first utterance;
recognize a first sequence of words from the first utterance;
interpret the first sequence of words to create a first interpretation;
interpret the first sequence of words to create an alternative interpretation;
receive a second utterance;
recognize a second sequence of words from the second utterance;
interpret the second sequence of words to create a second interpretation;
identify that the second interpretation matches the alternative interpretation; and
conclude that the first interpretation had an interpretation error.
24 . The non-transitory computer readable medium of claim 23 , wherein the code, if executed by the at least one computer processor, would further cause the virtual assistant to display results of the first interpretation and results of the alternative interpretation to the user.
25 . The non-transitory computer readable medium of claim 14 , wherein the code, if executed by the at least one computer processor, would further cause the virtual assistant to display the first sequence of words and the alternative sequence of words to the user.
26 . The non-transitory computer readable medium of claim 14 , wherein the code, if executed by the at least one computer processor, would further cause the virtual assistant to:
identify the presence of one or more indicator words in the second sequence of words; and
discard the indicator words prior to identifying that the second sequence of words matches the alternative sequence of words.
27 . A method of identifying speech recognition errors, the method comprising:
receiving a first utterance;
recognizing a first sequence of words and an alternative sequence of words from the first utterance;
receiving a second utterance;
recognizing a second sequence of words from the second utterance;
identifying that the second sequence of words matches the alternative sequence of words; and
concluding that the first sequence of words had a speech recognition error.
28 . The method of claim 27 further comprising:
displaying the first sequence of words and the alternative sequence of words to the user.
29 . The method of claim 27 further comprising:
identifying the presence of one or more indicator words in the second sequence of words; and
discarding the indicator words prior to identifying that the second sequence of words matches the alternative sequence of words.
30 . A method of identifying speech recognition errors, the method comprising:
receiving a first utterance;
recognizing a first sequence of words from the first utterance;
interpreting the first sequence of words to create a first interpretation;
interpreting the first sequence of words to create an alternative interpretation;
receiving a second utterance;
recognizing a second sequence of words from the second utterance;
interpreting the second sequence of words to create a second interpretation;
identifying that the second interpretation matches the alternative interpretation; and
concluding that the first interpretation had an interpretation error.
31 . The method of claim 30 further comprising:
displaying results of the first interpretation and results of the alternative interpretation to the user.
32 . An error-detecting speech recognition device comprising:
a speech recognition module that:
from a first speech utterance, produces a first sequence of words and an alternative sequence of words; and
from a second speech utterance, produces a second sequence of words; and
an identification module that identifies that the second sequence of words matches the alternative sequence of words,
wherein it can be concluded that the first sequence of words had a speech recognition error.
33 . The error-detecting speech recognition device of claim 32 further comprising:
a module for displaying the first sequence of words and the alternative sequence of words to the user.
34 . The error-detecting speech recognition device of claim 32 wherein the identification module:
identifies the presence of one or more indicator words in the second sequence of words; and
discards the indicator words prior to identifying that the second sequence of words matches the alternative sequence of words.
35 . An error-detecting speech recognition device comprising:
a speech recognition module that:
from a first speech utterance, produces a first sequence of words and an alternative sequence of words; and
from a second speech utterance, produces a second sequence of words;
an interpretation module that:
interprets the first sequence of words to create a first interpretation;
interprets the alternative sequence of words to create an alternative interpretation; and
interprets the second sequence of words to create a second interpretation; and
an identification module that identifies that the second interpretation matches the alternative interpretation,
wherein it can be concluded that the first sequence of words had a speech recognition error.
36 . The error-detecting speech recognition device of claim 35 further comprising a module for displaying results of the first interpretation and results of the alternative interpretation to the user.