Systems and methods for expressive language, developmental disorder, and emotion assessment, and contextual feedback

• US 10,223,934 B2
• Filed: 05/30/2016
• Issued: 03/05/2019
• Est. Priority Date: 09/16/2004
• Status: Active Grant

• Current Assignee: LENA Foundation
• Original Assignee: LENA Foundation
• Inventors: Paul, Terrance D.
• Primary Examiner(s): Ahmed, Masud
• Application Number:
  US15/168,153
• Publication Number:
  US 20160351074A1
• Time in Patent Office: 1,009 Days
• Field of Search: 434169, 704235
• CPC Class Codes: A61B 2503/04 Babies, e.g. for SIDS detection
• CPC Class Codes: A61B 2503/06 Children, e.g. for attention deficit diagnosis
• CPC Class Codes: A61B 5/168 Evaluating attention deficit, hyperactivity
• CPC Class Codes: A61B 5/4803 Speech analysis specially adapted for diagnostic purposes
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G09B 19/04 Speaking with audible presentation of the material to be studied G09B5/04
• CPC Class Codes: G10L 25/03 characterised by the type of extracted parameters
• CPC Class Codes: G10L 25/60 for measuring the quality of voice signals
• CPC Class Codes: G10L 25/63 for estimating an emotional state
• CPC Class Codes: G10L 25/66 for extracting parameters related to health condition detecting or measuring for diagnostic purposes A61B5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

In some embodiments, a method that includes capturing sound in a natural language environment using at least one sound capture device that is located in the natural language environment. The method also can include analyzing a sound signal from the sound captured by the at least one sound capture device to determine at least one characteristic of the sound signal. The method additionally can include reporting metrics that quantify the at least one characteristic of the sound signal. The metrics of the at least one characteristic can include a quantity of words spoken by one or more first persons in the natural language environment. Other embodiments are provided.

Claims

1. A system comprising:

a sound capture component configured to capture sounds produced by one or more first persons and a plurality of other sound sources in a natural language environment, wherein the plurality of other sound sources includes people other than the one or more first persons;

a sound processor coupled to the sound capture component and configured to analyze a sound signal from the sound capture component and identify characteristics from the sound signal; and

a reporting component configured to be in data communication with the sound processor and configured to report metrics that quantify the characteristics of the sound signal,

wherein;

the metrics of the characteristics comprise a quantity of words spoken by the one or more first persons in the natural language environment.

2. The system of claim 1, wherein the sound capture component is located proximate to a key child who is different from the one or more first persons.

3. The system of claim 1, wherein the sound capture component and the sound processor are integrated as a single device.

4. The system of claim 1, wherein the reporting component logs metrics for review at a time after the sound is captured.

5. The system of claim 1, wherein each of the one or more first persons is an adult.

6. The system of claim 5, wherein the sound capture component is located proximate to a key child who is different from the one or more first persons.

7. The system of claim 1, wherein the metrics of the characteristics comprise a frequency of conversational turn-taking.

8. The system of claim 1, wherein the metrics of the characteristics comprise a duration of conversational turn-taking.

9. A method comprising:

capturing sound in a natural language environment using at least one sound capture device that is located in the natural language environment;

analyzing a sound signal from the sound captured by the at least one sound capture device to determine at least one characteristic of the sound signal; and

reporting metrics that quantify the at least one characteristic of the sound signal,

wherein;

the metrics of the at least one characteristic comprise a quantity of words spoken by one or more first persons in the natural language environment.

10. The method of claim 9, wherein the at least one sound capture device is located proximate to a key child who is different from the one or more first persons.

11. The method of claim 9, wherein each of the one or more first persons is an adult.

12. The method of claim 9 further comprising logging the metrics in a log for subsequent analysis and presentation to a user.

13. The method of claim 11, wherein the at least one sound capture device is located proximate to a key child who is different from the one or more first persons.

14. The method of claim 9, wherein the at least one sound capture device is integrated as a single device with a sound processor that analyzes the sound signal to determine the at least one characteristic of the sound signal.

15. The method of claim 9, wherein the metrics of the at least one characteristic comprise a frequency of conversational turn-taking.

16. The method of claim 9, wherein the metrics of the at least one characteristic comprise a duration of conversational turn-taking.