Method and apparatus for searching for music based on speech recognition

• US 20080249770A1
• Filed: 08/20/2007
• Published: 10/09/2008
• Est. Priority Date: 01/26/2007
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Han, Ick-sang, Kim, Jeong-su, Kim, Kyu-hong
• Application Number:
  US11/892,137
• Publication Number:
  US 20080249770A1
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G06F 16/632 Query formulation
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/68 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G10L 15/08 Speech classification or search
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models

Abstract

Provided is a method and apparatus for searching music based on speech recognition. By calculating search scores with respect to a speech input using an acoustic model, calculating preferences in music using a user preference model, reflecting the preferences in the search scores, and extracting a music list according to the search scores in which the preferences are reflected, a personal expression of a search result using speech recognition can be achieved, and an error or imperfection of a speech recognition result can be compensated for.

Claims

1. A method of searching music based on speech recognition, the method comprising:

(a) calculating search scores with respect to a speech input using an acoustic model;

(b) calculating preferences in music using a user preference model and reflecting the preferences in the search scores; and

(c) extracting a music list according to the search scores in which the preferences are reflected.

2. The method of claim 1, wherein (b) comprises calculating search scores in which the preferences are reflected by linearly combining the search scores and the preferences.

3. The method of claim 1, wherein (a) further comprises calculating grades for reflecting the preferences in the search scores using a world model in which quality of the input speech is modeled and stored.

4. The method of claim 3, wherein the world model is a Guassian Mixture Model (GMM) of the quality of the input speech.

5. The method of claim 1, wherein (a) further comprises calculating grades for reflecting the preferences in the search scores by calculating likelihoods of monophones of the acoustic model.

6. The method of claim 1, wherein (a) comprises calculating the search scores by normalizing the number of frames of the input speech.

7. The method of claim 1, wherein (b) comprises adjusting grades for reflecting the preferences in the search scores.

8. The method of claim 1, wherein (b) comprises calculating search scores on which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

w ) } N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, and α

_user denotes a constant indicating how much a music preference is reflected.

9. The method of claim 1, wherein (b) comprises calculating search scores on which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

w ) } - log 



P 

( x 

λ

world ) N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, α

_user denotes a constant indicating how much a music preference is reflected, and λ

_world denotes a world model used to remove an affection due to a change in speaking environment.

10. The method of claim 1, wherein (b) comprises calculating search scores in which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

) } - log 



P 

( x 

λ

phone ) N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, α

_user denotes a constant indicating how much a music preference is reflected, and λ

_phone denotes an acoustic model formed with monophones to remove an affection due to a change in speaking environment.

11. A computer readable recording medium storing a computer readable program for executing the method of any one of claims 1 through 10.

12. An apparatus for searching music based on speech recognition, the apparatus comprising:

a user preference model modeling and storing a user'"'"'s favored music; and

a search unit calculating search scores with respect to speech input using an acoustic model, calculating preferences in music using the user preference model, and extracting a music list by reflecting the preferences in the search scores.

13. The apparatus of claim 12, wherein the search unit comprises:

a search score calculator calculating search scores with respect to speech input using the acoustic model;

a preference calculator calculating preferences in music using the user preference model;

a synthesis calculator reflecting the preferences in the search scores; and

an extractor extracting a music list according to search scores in which the preferences are reflected.

14. The apparatus of claim 12, further comprising a world model in which quality of the input speech is modeled,

wherein the search unit further comprises a reflection calculator calculating reflection grades of the search scores using the world model.

15. The apparatus of claim 14, wherein the reflection calculator calculates grades for reflecting the preferences in the search scores by calculating likelihoods of monophones of the acoustic model.

16. The apparatus of claim 12, wherein the search unit calculates search scores on which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

w ) } N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, and α

_user denotes a constant indicating how much a music preference is reflected.

17. The apparatus of claim 12, wherein the search unit calculates search scores on which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

w ) } - log 



P 

( x 

λ

world ) N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, α

_user denotes a constant indicating how much a music preference is reflected, and λ

_world denotes a world model used to remove an affection due to a change in speaking environment.

18. The apparatus of claim 12, wherein the search unit calculates search scores in which the preferences are reflected using the equation

$\mathrm{Score}$

( W ) = max w ∈

W 

{ log 



P 

( x 

λ

w ) } - log 



P 

( x 

λ

phone ) N frame + α

user ·

log 



P 

( W 

U ) ,

where N_frame denotes the length of an input speech feature vector, α

_user denotes a constant indicating how much a music preference is reflected, and λ

_phone denotes an acoustic model formed with monophones to remove an affection due to a change in speaking environment.

19. An apparatus for searching music based on speech recognition, which comprises a feature extractor, a search unit, an acoustic model, a lexicon model, a language model, and a music database (DB), the apparatus comprising a user preference model modeling a user'"'"'s favored music,

wherein the search unit calculates search scores with respect to a speech feature vector input from the feature extractor using the acoustic model, calculates preferences in music stored in the music DB using the user preference model, and extracts a music list matching the input speech by reflecting the preferences in the search scores.

20. The apparatus of claim 19, further comprising a world model in which quality of the input speech is modeled and stored,

wherein the search unit calculates reflection grades of the search scores using the world model.