IP Library Granted Patent US 10,573,336
Granted Patent B2
US 10,573,336 · App. 15/898,512 · Granted Feb 25, 2020

System and method for assessing expressive language development of a key child

Inventors: Terrance D. Paul; Dongxin D. Xu (Boulder, CO); Sharmistha Sarkar Gray (Dracut, MA); Umit Yapanel (Sunnyvale, CA); Jill S. Gilkerson (Boulder, CO); Jeffrey A. Richards (Boulder, CO)
Assignee: LENA FOUNDATION
G10L25/63A61B5/165A61B5/4803A61B5/7264G10L15/02G10L15/063G10L15/075G10L15/14G10L17/16G10L17/26G10L25/66A61B2503/06G10L15/00G10L2015/022G10L2015/0631
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Quick Facts
Patent No.
US 10,573,336
App. No.
15/898,512
Granted
Feb 25, 2020
Kind
B2
Abstract

A method of assessing expressive language development of a key child. The method can include processing an audio recording taken in a language environment of the key child to identify segments of the audio recording that correspond to vocalizations of the key child. The method also can include applying an adult automatic speech recognition phone decoder to the segments of the audio recordings to identify each occurrence of a plurality of phone categories and to determine a duration for each of the plurality of phone categories. The method additionally can include determining a duration distribution for the plurality of phone categories based on the durations for the plurality of phone categories. The method further can include using the duration distribution for the plurality of phone categories in an age-based model to assess the expressive language development of the key child. The age-based model is selected based on a chronological age of the key child and the age-based model includes a plurality of different weights associated with the plurality of phone categories. Other embodiments are described.

Claims (56)

1. A system for assessing expressive language development of a key child, comprising:

one or more processors;

one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform:

processing an audio recording taken in a language environment of the key child to identify segments of the audio recording that correspond to vocalizations of the key child;

applying an adult automatic speech recognition phone decoder to the segments of the audio recording that correspond to the vocalizations of the key child to identify each occurrence of a plurality of phone categories in the segments and to determine a respective duration for each of the plurality of phone categories in the segments, wherein each of the plurality of phone categories corresponds to a respective pre-defined speech sound that is a smallest phonetic unit capable of conveying a distinction in meaning in a predetermined language, and wherein the plurality of phone categories are a first subset of the vocalizations of the key child; and

determining a duration distribution for the plurality of phone categories based on the respective durations for the plurality of phone categories; and

using the duration distribution for the plurality of phone categories in an age-based model to assess the expressive language development of the key child, wherein the age-based model is selected based on a chronological age of the key child, and the age-based model comprises a plurality of different weights associated with the plurality of phone categories.

2. The system of claim 1 , wherein:

applying the adult automatic speech recognition phone decoder comprises identifying occurrences of a plurality of non-phone categories;

each of the plurality of non-phone categories corresponds to a respective pre-defined non-speech sound; and

the plurality of non-phone categories are a second subset of the vocalizations of the key child.

3. The system of claim 1 , wherein there is a correlation between the respective duration for each of the plurality of phone categories to chronological age.

4. The system of claim 1 , wherein the age-based model is based on an age in months.

5. The system of claim 1 , wherein the age-based model is an adjustment of the chronological age of the key child.

6. The system of claim 1 , wherein using the duration distribution for the plurality of phone categories in the age-based model to assess the expressive language development of the key child comprises computing an estimated developmental age for the key child based at least in part on the plurality of different weights associated with the plurality of phone categories.

7. The system of claim 6 , wherein the computing instructions are further configured to perform using the estimated developmental age for the key child to determine an estimated mean length of utterance for the key child.

8. The system of claim 6 , wherein the computing instructions are further configured to perform:

processing at least one additional audio recording taken in the language environment of the key child to identify additional segments of the audio recording that correspond to the vocalizations of the key child;

applying the adult automatic speech recognition phone decoder to the additional segments of the audio recording to identify each occurrence of the plurality of phone categories in the additional segments and to determine an additional respective duration for each of the plurality of phone categories in the additional segments;

determining an additional duration distribution for the plurality of phone categories based on the additional respective durations for the plurality of phone categories;

using the additional duration distribution for the plurality of phone categories in the age-based model to assess the expressive language development of the key child and to generate an additional estimated developmental age for the key child based at least in part on the plurality of different weights associated with the plurality of phone categories; and

averaging the estimated developmental age and the additional estimated developmental age.

9. The system of claim 1 , wherein:

applying the adult automatic speech recognition phone decoder comprises identifying occurrences of a plurality of non-phone categories, wherein each of the plurality of non-phone categories corresponds to a respective pre-defined non-speech sound, and wherein the plurality of non-phone categories are a second subset of the vocalizations of the key child;

the age-based model is an adjustment of the chronological age of the key child; and

the age-based model is based on an age in months.

10. The system of claim 1 , wherein the computing instructions are further configured to perform:

computing a standard score and a developmental age for the key child; and

generating an output comprising at least one of: the standard score or the developmental age.

11. A method of assessing expressive language development of a key child, comprising:

processing an audio recording taken in a language environment of the key child to identify segments of the audio recording that correspond to vocalizations of the key child;

applying an adult automatic speech recognition phone decoder to the segments of the audio recording to identify each occurrence of a plurality of phone categories in the segments and to determine a respective duration for each of the plurality of phone categories in the segments, wherein each of the plurality of phone categories corresponds to a respective pre-defined phoneme speech sound that is a smallest phonetic unit capable of conveying a distinction in meaning in a predetermined language, and wherein the plurality of phone categories are a first subset of the vocalizations of the key child;

determining a duration distribution for the plurality of phone categories based on the respective durations for the plurality of phone categories; and

using the duration distribution for the plurality of phone categories in an age-based model to assess the expressive language development of the key child, wherein the age-based model is selected based on a chronological age of the key child, and the age-based model comprises a plurality of different weights associated with the plurality of phone categories.

12. The method of claim 11 , wherein:

applying the adult automatic speech recognition phone decoder comprises identifying occurrences of a plurality of non-phone categories;

each of the plurality of non-phone categories corresponds to a respective pre-defined non-speech sound; and

the plurality of non-phone categories are a second subset of the vocalizations of the key child.

13. The method of claim 11 , wherein there is a correlation between the respective duration for each of the plurality of phone categories to chronological age.

14. The method of claim 11 , wherein the age-based model is based on an age in months.

15. The method of claim 11 , wherein the age-based model is an adjustment of the chronological age of the key child.

16. The method of claim 11 , wherein using the duration distribution for the plurality of phone categories in the age-based model to assess the expressive language development of the key child comprises computing an estimated developmental age for the key child based at least in part on the plurality of different weights associated with the plurality of phone categories.

17. The method of claim 16 further comprising using the estimated developmental age for the key child to determine an estimated mean length of utterance for the key child.

18. The method of claim 16 further comprising:

processing, using an audio engine, at least one additional audio recording taken in the language environment of the key child to identify additional segments of the audio recording that correspond to the vocalizations of the key child;

applying the adult automatic speech recognition phone decoder to the additional segments of the audio recording to identify each occurrence of the plurality of phone categories in the additional segments and to determine an additional respective duration for each of the plurality of phone categories in the additional segments;

determining an additional duration distribution for the plurality of phone categories based on the additional respective durations for the plurality of phone categories;

using the additional duration distribution for the plurality of phone categories in the age-based model to assess the expressive language development of the key child and to generate an additional estimated developmental age for the key child based at least in part on the plurality of different weights associated with the plurality of phone categories; and

averaging the estimated developmental age and the additional estimated developmental age.

19. The method of claim 11 , wherein:

applying the adult automatic speech recognition phone decoder comprises identifying occurrences of a plurality of non-phone categories, wherein each of the plurality of non-phone categories corresponds to a respective pre-defined non-speech sound, and wherein the plurality of non-phone categories are a second subset of the vocalizations of the key child;

the age-based model is an adjustment of the chronological age of the key child; and

the age-based model is based on an age in months.

20. The method of claim 11 further comprising:

computing a standard score and a developmental age for the key child; and

generating an output comprising at least one of: the standard score or the developmental age.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 21, 2018
From: PAUL, TERRANCE D.; XU, DONGXIN D.; GRAY, SHARMISTHA S.; YAPANEL, UMIT; GILKERSON, JILL S.; RICHARDS, JEFFREY A.
To: INFOTURE, INC.
Reel/Frame 044987/0151 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 21, 2018
From: INFOTURE, INC.
To: LENA FOUNDATION
Reel/Frame 044987/0273 →
Continuity (7)
Continuation 14997179 · Jan 15, 2016
Division 12359124 · Jan 23, 2009
Continuation In Part 12109785 · Apr 25, 2008
Continuation In Part 12018647 · Jan 23, 2008
Provisional Application 60886122 · Jan 23, 2007
Provisional Application 60886167 · Jan 23, 2007
Related Publication 20180174601A1 · Jun 21, 2018