MISSPEAK RESOLUTION IN NATURAL LANGUAGE UNDERSTANDING FOR A MAN-MACHINE INTERFACE
Misspeaking is resolved in a natural language understanding system to interface with a machine. In one example, a user speech utterance is received. A sequence of classifiers is applied to words of the utterance to determine a meaning of the utterance. The meaning in interpreted as a command and the command is applied to a device for execution. The classifiers may include a first classifier to determine an intended function that is a subject of the utterance, a second classifier to determine words with properties that are related to the intended function, and a third classifier to select a property to apply to the function.
1 . A method comprising:
receiving a user speech utterance;
applying a sequence of classifiers to words of the utterance to determine a meaning of the utterance;
interpreting the determined meaning as a command; and
applying the command to a device for execution.
2 . The method of claim 1 , wherein applying a sequence of classifiers comprises applying a first classifier to determine an intended function that is a subject of the utterance and then applying a second classifier to determine words with properties that are related to the intended function.
3 . The method of claim 2 , further comprising choosing a word to apply to the intended function by applying a neural network to the words with properties that are related to the function to choose a word.
4 . The method of claim 3 , wherein the words with properties are structured as a bag of words with sequence information.
5 . The method of claim 4 , wherein bag of words features are presented as vector representatives of word collections.
6 . The method of claim 2 , further comprising selecting a function and application using the intended function.
7 . The method of claim 1 , wherein the sequence of classifiers comprise a user intent detection, followed by a word property detection, followed by a property selection made by applying the intent to words having appropriate properties.
8 . The method of claim 7 , wherein the intent detection classifier determines an intent of the user by applying probabilities of possible intents to a bag-of-words representation of the utterance and selecting a most probable intent.
9 . The method of claim 7 , wherein the word property detection classifier associates words of the utterance with properties represented by the words.
10 . The method of claim 9 , wherein the word property detection classifier detects word properties using a vocabulary and a neural network.
11 . The method of claim 9 , wherein the word property detection classifier validates each detected property against a natural language understanding module to ensure that the corresponding word can be evaluated based on the property.
12 . The method of claim 1 , further comprising the classifiers of the sequence of classifiers using data driven machine learning.
13 . The method of claim 1 , further comprising using temporal features of the words to distinguish between properties and then revising properties in the utterance.
14 . The method of claim 1 , wherein applying a sequence of classifier comprises classifying at least a portion of the words, the method further comprising applying a pre-defined rule to words of the same classification.
15 . The method of claim 1 , wherein the pre-defined rule is to use the last word of the same classification as the meaning.
16 . An apparatus comprising:
an automatic speech recognition module to receive a user speech utterance and determine a sequence of words;
a natural language understanding module to apply a sequence of classifiers to the words of the utterance to determine a meaning of the utterance; and
an application module to interpret the meaning as a command and to apply the command to a device for execution.
17 . The apparatus of claim 17 , wherein the natural language understanding module comprises a neural network to determine properties of words of the utterance.
18 . The apparatus of claim 17 , wherein the natural language understanding module classifier associates words of the utterance with properties represented by the words using temporal features of the words to distinguish between properties.
19 . A speech operated system comprising:
a microphone to receive a speech utterance from a user;
an automatic speech recognition module to receive the speech utterance and determine a sequence of words;
a natural language understanding module to apply a sequence of classifiers to the words of the utterance to determine a meaning of the utterance;
an application module to interpret the meaning as a command; and
an actuator to execute the command.
20 . The system of claim 19 , wherein the sequence of classifiers comprise:
a first classifier to determine an intended function that is a subject of the utterance;
a second classifier to determine words with properties that are related to the intended function; and
a third classifier to select a property to apply to the function.