SYSTEM AND METHOD FOR DETECTION AND ANALYSIS OF SPEECH

• US 20080235016A1
• Filed: 01/23/2008
• Published: 09/25/2008
• Est. Priority Date: 01/23/2007
• Status: Active Grant

• Current Assignee: LENA Foundation
• Original Assignee: Infoture, Inc.
• Inventors: Xu, Dongxin, Paul, Terrance, Gray, Sharmistha, Yapenel, Umit
• Granted Patent: US 8,078,465 B2
• Time in Patent Office: Days
• US Class Current: 704/246
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G10L 15/14 using statistical models, e.g. Hidden Markov Models [HMMs] G10L15/18 takes precedence
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

Certain aspects and embodiments of the present invention are directed to systems and methods for monitoring and analyzing the language environment and the development of a key child. A key child'"'"'s language environment and language development can be monitored without placing artificial limitations on the key child'"'"'s activities or requiring a third party observer. The language environment can be analyzed to identify words, vocalizations, or other noises directed to or spoken by the key child, independent of content. The analysis can include the number of responses between the child and another, such as an adult and the number of words spoken by the child and/or another, independent of content of the speech. One or more metrics can be determined based on the analysis and provided to assist in improving the language environment and/or tracking language development of the key child.

Claims

1. A method comprising:

capturing an audio recording from a language environment of a key child,

segmenting the audio recording into a plurality of segments;

identifying a segment ID for each of the plurality of segments, the segment ID identifying a source for audio in the segment;

identifying a plurality of key child segments from the plurality of segments, each of the plurality of key child segments having the key child as the segment ID;

estimating key child segment characteristics based in part on at least one of the plurality of key child segments, wherein the key child segment characteristics are estimated independent of content of the plurality of key child segments;

determining at least one metric associated with the language environment using the key child segment characteristics; and

outputting the at least one metric.

2. The method of claim 1, further comprising:

identifying a plurality of adult segments from the plurality of segments, each of the plurality of adult segments having the adult as the segment ID;

estimating adult segment characteristics based in part on at least one of the plurality of adult segments, wherein the adult segment characteristics are estimated independent of content of the plurality of adult segments; and

wherein determining at least one metric associated with the language environment comprises using the adult segment characteristics.

3. The method of claim 2, wherein adult segment characteristics comprise at least one of:

a word count;

a duration of speech;

a vocalization count; and

a parentese count.

4. The method of claim 2, wherein the at least one metric comprises at least one of:

number of key child vocalizations in a pre-set time period.

number of conversational turns, wherein the conversational turns comprise a sound from one of the adult or key child and a response to the sound from one of the adult or key child; and

number of adult words directed to the key child in a pre-set time period.

5. The method of claim 1, wherein segmenting the audio recording into the plurality of segments and identifying the segment ID for each of the plurality of segments comprises:

using a Minimum Duration Gaussian Mixture Model (MD-GMM).

6. The method of claim 2, wherein using the MD-GMM comprises:

performing a first segmentation and a first segment ID using a first MD-GMM, the first MD-GMM comprising a plurality of models;

generating a second MD-GMM by modifying at least one of the plurality of models; and

segmenting the audio recording into the plurality of segments and identifying the segment ID for each of the plurality of segments using the second MD-GMM.

7. The method of claim 6, wherein the plurality of models comprise a key child model, an electronic device model, and an adult model, wherein:

the key child model comprises criteria associated with sounds from a child;

the electronic device model comprises criteria associated with sounds from an electronic device; and

the adult model comprises criteria associated with sounds from adults.

8. The method of claim 7, further comprising at least one of:

modifying the key child model using an age-dependent key child model, wherein the age-dependent key child model comprises criteria associated with sounds from children of a plurality of ages;

modifying the electronic device model;

modifying at least one of the key child model and the adult model using a loudness/clearness detection model, wherein the loudness/clearness detection model comprises a Likelihood Ratio Test; and

modifying at least one of the key child model and the adult model using a parentese model, wherein the parentese model comprises complexity levels associated with sounds of adults.

9. The method of claim 1, further comprising:

classifying each of the plurality of key child segments into one of;

vocalizations;

cries;

vegetative sounds;

fixed signal sounds; and

wherein key child segment characteristics are estimated using key child segments classified into at least one of vocalizations and cries.

10. The method of claim 9, wherein classifying each of the plurality of key child segments comprises using at least one of rule-based analysis and statistical processing.

11. The method of claim 1, wherein key child segment characteristics comprises at least one of:

duration of cries;

number of squeals;

number of growls;

presence of canonical syllables;

number of canonical syllables;

presence of repetitive babbles;

number of repetitive babbles;

presence of protophones;

number of protophones;

duration of protophones;

presence of phoneme-like sounds;

number of phoneme-like sounds;

duration of phoneme-like sounds;

presence of phonemes;

number of phonemes;

duration of phonemes;

word count; and

vocalization count.

12. A method comprising:

capturing an audio recording from a language environment of a key child,

segmenting the audio recording into a plurality of segments and identifying a segment ID for at least one of the plurality of segments using a Minimum Duration Gaussian Mixture Model (MD-GMM), the segment ID identifying a key child;

estimating key child segment characteristics based in part on the at least one of the plurality of segments, wherein the key child segment characteristics are estimated independent of content of the plurality of segments;

determining at least one metric associated with the language environment using the key child segment characteristics; and

outputting the at least one metric.

13. The method of claim 12, wherein the key child segment characteristics comprises a number of vowels and a number of consonants in the at least one of the plurality of segments.

14. The method of claim 13, wherein determining at least one metric associated with the language environment using the key child segment characteristics comprises:

comparing the number of vowels and number of consonants in the at least one of the plurality of segments to attributes associated with a native language of the key child to determine a number of words spoken by the key child.

15. The method of claim 14, wherein the MD-GMM comprises a key child model;

modifying the key child model using an age-dependent key child model; and

wherein segmenting the audio recording into the plurality of segments and identifying the segment ID for at least one of the plurality of segments using the MD-GMM comprises using the MD-GMM comprising the modified key child model.

16. The method of claim 15, wherein the age-dependent key child model comprises:

a first model group comprising characteristics of sounds of children of a first age; and

a second model group comprising characteristics of sounds of children of a second age.

17. A system comprising:

a recorder adapted to capture audio recordings from a language environment of a key child and provide the audio recordings to a processor-based device; and

the processor-based device comprising an application having an audio engine adapted to segment the audio recording into a plurality of segments and identify a segment ID for each of the plurality of segments, wherein at least one of the plurality of segments is associated with a key child segment ID, the audio engine being further adapted to;

estimate key child segment characteristics based in part on the at least one of the plurality of segments, wherein the audio engine estimates key child segment characteristics independent of content of the at least one of the plurality of segments;

determine at least one metric associated with the language environment using the key child segment characteristics; and

output the at least one metric to an output device.

18. The system of claim 17, wherein the audio engine segments the audio recording and identifies a segment ID for each of the plurality of segments using a Minimum Duration Gaussian Mixture Model (MD-GMM).

19. The system of claim 18, wherein the audio engine uses the MD-GMM by:

performing a first segmentation and a first segment ID using a first MD-GMM, the first MD-GMM comprising a plurality of models;

generating a second MD-GMM by modifying at least one of the plurality of models; and

segmenting the audio recording into the plurality of segments and identifying the segment ID for each of the plurality of segments using the second MD-GMM.

20. The system of claim 19, wherein the plurality of models comprise a key child model, an electronic device model, and an adult model.

21. The system of claim 20, further comprising at least one of:

the audio engine adapted to modify the key child model using an age-dependent key child model, the age-dependent key child model comprising;

a first model group comprising characteristics of sounds of children of a first age; and

a second model group comprising characteristics of sounds of children of a second age;

the audio engine adapted to modify the electronic device model, the electronic device model comprising criteria associated with sounds generated by an electronic device;

the audio engine adapted to modify at least one of the key child model and the adult model using a loudness/clearness detection model, the loudness/clearness detection model comprising a Likelihood Ratio Test; and

the audio engine adapted to modify at least one of the key child model and the adult model using a parentese model, the parentese model comprising a complexity level of speech associated with adult sounds.

22. The system of claim 18, wherein the audio engine uses the MD-GMM by:

scoring each of the plurality of segments using log-likelihood scoring and a plurality of models; and

analyzing the scored plurality of segments to assign the segment ID to each of the plurality of segments.

23. The system of claim 18, wherein the MD-GMM comprises a plurality of models, each model comprising criteria associated with sounds and sources of sounds, the plurality of models comprising at least one of:

a key child model comprising criteria associated with sounds from the key child;

an adult model comprising criteria associated with sounds from an adult;

a noise model comprising criteria associated with sounds attributable to noise;

an electronic device model comprising criteria associated with sounds from an electronic device;

an other child model comprising criteria associated with sounds from a child other than the key child;

an age-dependent key child model comprising criteria associated with sounds from key children of plurality of ages; and

a parentese model comprising a complexity level characteristics of sounds of adults.

24. The system of claim 23, wherein the audio engine is adapted to:

use the other child model to identify at least one of the plurality of segments comprising sounds from a child other than the key child; and

assign an other child segment ID to the identified at least one of the plurality of segments.

25. The system of claim 23, wherein the audio engine is adapted to:

use the noise model to identify at least one of the plurality of segments comprising sounds from noise; and

assign a noise segment ID to the identified at least one of the plurality of segments.

26. The system of claim 23, wherein the audio engine is adapted to:

use the key child model to identify at least one of the plurality of segments comprising sounds with characteristics associated with the sounds from the key child; and

assign the key child segment ID to the identified at least one of the plurality of segments;

27. The system of claim 23, wherein the audio engine is adapted to:

use the adult model to identify at least one of the plurality of segments comprising sounds from an adult; and

assign an adult segment ID to the identified at least one of the plurality of segments.

28. The system of claim 23, wherein the audio engine is adapted to:

use the electronic model to identify at least one of the plurality of segments comprising sounds having criteria associated with electronic device sounds, the criteria associated with electronic device sounds comprising at least one of;

duration longer than a pre-set period; and

a series of segments having a pre-set source pattern; and

assign a noise segment ID to the identified at least one of the plurality of segments.

29. The system of claim 23, wherein the age-dependent key child model comprises:

a first model group comprising criteria of sounds of children of a first age; and

a second model group comprising criteria of sounds of children of a second age; and

wherein the audio engine is adapted to;

select one of the first model group and the second model group based on information associated with the key child;

use the selected model group to identify at least one of the plurality of segments comprising sounds having characteristics of the selected model group; and

assign the key child segment ID to the identified at least one of the plurality of segments

30. The system of claim 23, wherein the audio engine is adapted to:

use the parentese model to identify at least one of the plurality of segments comprising sounds having the complexity level characteristics; and

assign an adult segment ID to the identified at least one of the plurality of segments.