SPEAKER ADAPTATION APPARATUS AND PROGRAM THEREOF

• US 20100169094A1
• Filed: 09/17/2009
• Published: 07/01/2010
• Est. Priority Date: 12/25/2008
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Ajmera, Jitendra, Lal, Partha, Akamine, Masami
• Application Number:
  US12/561,445
• Publication Number:
  US 20100169094A1
• Time in Patent Office: Days
• US Class Current: 704/244
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/144 Training of HMMs

Abstract

A speaker adaptation apparatus includes an acquiring unit configured to acquire an acoustic model including HMMs and decision trees for estimating what type of the phoneme or the word is included in a feature value used for speech recognition, the HMMs having a plurality of states on a phoneme-to-phoneme basis or a word-to-word basis, and the decision trees being configured to reply to questions relating to the feature value and output likelihoods in the respective states of the HMMs, and a speaker adaptation unit configured to adapt the decision trees to a speaker, the decision trees being adapted using speaker adaptation data vocalized by the speaker of an input speech.

Claims

1. A speaker adaptation apparatus comprising:

an acquiring unit configured to acquire an acoustic model including HMMs and decision trees for estimating what type of the phoneme or the word is included in a feature value used for speech recognition, the HMMs having a plurality of states on a phoneme-to-phoneme basis or a word-to-word basis, and the decision trees being configured to reply to questions relating to the feature value and output likelihoods in the respective states of the HMMs; and

a speaker adaptation unit configured to adapt the decision trees to a speaker, the decision trees being adapted using speaker adaptation data vocalized by the speaker of an input speech.

2. The apparatus according to claim 1, wherein

the speaker adaptation unit combines a parameter of the decision tree, a parameter of a speaker-independent decision tree which does not depend on the speaker, and a parameter of a speaker-dependent decision tree which depends on the speaker created using the speaker adaptation data to adapt the speaker.

3. The apparatus according to claim 2, wherein

the parameter includes a question parameter relating to the question and a likelihood parameter indicating the likelihood, and

the speaker adaptation unit uses the speaker adaptation data to combine the question parameters of respective nodes and the likelihood parameters of leaves of the speaker-independent decision trees with the question parameters of respective nodes and the likelihood parameters of leaves of the speaker-dependent decision tree respectively and create a speaker adaptation decision tree as a decision tree adapted to the speaker and achieves the speaker adaptation.

4. The apparatus according to claim 2, wherein

the speaker adaptation unit combines the parameter of the speaker-independent decision tree and the parameter of the speaker-dependent decision tree on the basis of a weight determined by using the speaker adaptation data to adapt the speaker.

5. The apparatus according to claim 1, wherein

the speaker adaptation unit

uses the speaker adaptation data of each of a plurality of the speakers to create respective speaker-dependent decision trees,

uses parameters of the respective speaker-dependent decision trees to create a plurality of specific speaker decision trees by a PCA, and

uses the speaker adaptation data to combine the likelihoods of the respective specific speaker decision trees to adapt the speakers.

6. A program stored in a computer readable medium, the program causing the computer to implement:

an acquiring function to acquire an acoustic model including HMMs and decision trees for estimating what type of the phoneme or the word is included in a feature value used for speech recognition, the HMMs having a plurality of states on a phoneme-to-phoneme basis or a word-to-word basis, and the decision trees being configured to reply to questions relating to the feature value and output likelihoods in the respective states of the HMMs; and

a speaker adaptation function to adapt the decision trees to a speaker, the decision trees being adapted using speaker adaptation data vocalized by the speaker of an input speech.

7. The program according to claim 6, wherein the speaker adaptation function combines a parameter of the decision tree, a parameter of a speaker-independent decision tree which does not depend on the speaker, and a parameter of a speaker-dependent decision tree which depends on the speaker created using the speaker adaptation data to adapt the speaker.

8. The program according to claim 7, wherein

the parameter includes a question parameter relating to the question and a likelihood parameter indicating the likelihood, and

the speaker adaptation function uses the speaker adaptation data to combine the question parameters of respective nodes and the likelihood parameters of leaves of the speaker-independent decision trees with the question parameters of respective nodes and the likelihood parameters of leaves of the speaker-dependent decision tree respectively and create a speaker adaptation decision tree as a decision tree adapted to the speaker and achieves the speaker adaptation.

9. The program according to claim 7, wherein

the speaker adaptation function combines the parameter of the speaker-independent decision tree and the parameter of the speaker-dependent decision tree on the basis of a weight determined by using the speaker adaptation data to adapt the speaker.

10. The program according to claim 6, wherein

the speaker adaptation function

uses the speaker adaptation data of each of a plurality of the speakers to create respective speaker-dependent decision trees,

uses parameters of the respective speaker-dependent decision trees to create a plurality of specific speaker decision trees by a PCA, and

uses the speaker adaptation data to combine the likelihoods of the respective specific speaker decision trees to adapt the speakers.

11. A speaker adaptation method comprising:

acquiring an acoustic model including HMMs and decision trees for estimating what type of the phoneme or the word is included in a feature value used for speech recognition, the HMMs having a plurality of states on a phoneme-to-phoneme basis or a word-to-word basis, and the decision trees being configured to reply to questions relating to the feature value and output likelihoods in the respective states of the HMMs; and

adapting the decision trees to a speaker, the decision trees being adapted using speaker adaptation data vocalized by the speaker of an input speech.