SYSTEMS AND METHODS FOR LANGUAGE AND SPEECH PROCESSING WITH ARTIFICIAL INTELLIGENCE
A language computing system includes one or more processing circuits having one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including obtaining one or more textual documents including information related to a company, generating a future pattern model describing patterns of the company, providing the one or more textual documents to the future pattern model to generate a predicted pattern of the company, and providing the predicted pattern to a user.
1 . A language computing system, the system comprising:
one or more processing circuits comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining one or more textual documents including information related to a company;
generating a future pattern model describing patterns of the company;
providing the one or more textual documents to the future pattern model to generate a predicted pattern of the company; and
providing the predicted pattern to a user.
2 . The language computing system of claim 1 , the operations further comprising:
extracting the information related to the company from the one or more textual documents; and
generating an ontological graph based on the extracted information, the ontological graph describing one or more relationships of the company.
3 . The language computing system of claim 2 , wherein the ontological graph is at least one of:
a domain graph;
a knowledge graph; or
a decision graph.
4 . The language computing system of claim 1 , the operations further comprising:
providing the predicted pattern to a second user for validation;
in response to receiving an indication that the predicted pattern is valid, the predicted pattern to the user; and
in response to receiving an indicated that the predicted pattern is not valid, retraining the future pattern model.
5 . The language computing system of claim 1 , the operations further comprising:
obtaining one or more classifiers;
generating a historical pattern model for estimating a probability that a particular textual document includes each of the one or more classifiers; and
providing the one or more textual documents to the historical pattern model to obtain a set of probabilities;
wherein the future pattern model is generated based on the set of probabilities.
6 . The language computing system of claim 1 , wherein the future pattern model is a long short-term memory model.
7 . The language computing system of claim 1 , wherein the predicted pattern is a predicted stock trend for the company.
8 . A method for generating pattern predictions, the method comprising:
obtaining one or more textual documents including information related to a company;
generating a future pattern model describing patterns of the company;
providing the one or more textual documents to the future pattern model to generate a predicted pattern of the company; and
providing the predicted pattern to a user.
9 . The method of claim 8 , further comprising:
extracting the information related to the company from the one or more textual documents; and
generating an ontological graph based on the extracted information, the ontological graph describing one or more relationships of the company.
10 . The method of claim 9 , wherein the ontological graph is at least one of:
a domain graph;
a knowledge graph; or
a decision graph.
11 . The method of claim 8 , further comprising:
providing the predicted pattern to a second user for validation;
in response to receiving an indication that the predicted pattern is valid, the predicted pattern to the user; and
in response to receiving an indicated that the predicted pattern is not valid, retraining the future pattern model.
12 . The method of claim 8 , further comprising:
obtaining one or more classifiers;
generating a historical pattern model for estimating a probability that a particular textual document includes each of the one or more classifiers; and
providing the one or more textual documents to the historical pattern model to obtain a set of probabilities;
wherein the future pattern model is generated based on the set of probabilities.
13 . The method of claim 8 , wherein the future pattern model is a long short-term memory model.
14 . The method of claim 8 , wherein the predicted pattern is a predicted stock trend for the company.
15 . An emotion recognition system for speech processing, the system comprising:
one or more processing circuits comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving a speech signal;
analyzing the speech signal at a frame-level to identify one or more low-level descriptors of the speech signal;
analyzing the speech signal at an utterance level to identify one or more utterance-level descriptors; and
determining an emotion associated with the speech signal based on the one or more low-level descriptors and the one or more utterance-level descriptors.
16 . The emotion recognition system of claim 15 , wherein the one or more low-level descriptors comprise at least one of:
a mel-frequency cepstral coefficient;
a linear prediction cepstral coefficient; or
a residual mel-frequency cepstral coefficient.
17 . The emotion recognition system of claim 15 , wherein analyzing the speech signal at the utterance level comprises:
training a long short-term memory (LSTM) autoencoder to predict itself; and
collecting a hidden representation at an end of the LSTM autoencoder, the hidden representation representing the speech signal.
18 . The emotion recognition system of claim 15 , the operations further comprising providing the determined emotion associated with the speech signal to a user.
19 . An impact analysis system, the system comprising:
one or more processing circuits comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
obtaining results of a customer survey;
identifying one or more topics based on the customer survey;
performing an impact analysis to calculate topic impact scores for each of the one or more topics, wherein a topic impact score for a topic indicates an impact of the topic on customer behavior changes;
generating an impact analysis report indicating the topic impact scores; and
providing the impact analysis report to a user.
20 . The impact analysis system of claim 19 , the operations further comprising identifying one or more factors and associated coefficients, wherein the topic impact scores are calculated with respect to the one or more factors and associated coefficients.
21 . The impact analysis of claim 20 , wherein performing the impact analysis comprises:
calculating a topic frequency difference as a difference between an overall topic frequency and a topic frequency by the one or more factors; and
calculating the topic impact scores based on the topic frequency difference and coefficients associated with the one or more factors.
22 . The impact analysis system of claim 19 , wherein the impact analysis report comprises at least one of:
a scatter plot illustrating the topic impact scores; or
a bar chart illustrating the topic impact scores.
23 . The impact analysis system of claim 19 , the operations further comprising auditing the impact analysis report for accuracy prior to providing the impact analysis report to the user.