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Using eigenvoices and nearest-neighbors in HMM-based cross-lingual speaker adaptation with limited data

Title	Using eigenvoices and nearest-neighbors in HMM-based cross-lingual speaker adaptation with limited data
Author	Sarfjoo, Seyyed Saeed, Demiroğlu, Cenk, King, S.
Publication Date:	2017-04
Publication Place	- IEEE
Subject	Cross lingual speaker adaptation, Statistical speech synthesis, Speaker adaptation, Nearest neighbour
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2329-9304
Record ID	ee9f3091-c0c9-4a59-92e6-4caaef7adf64
Library Location	Electrical & Electronics Engineering
Date	2017-04
Notes	European Commission ; TUBITAK
Sample Text	Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.
DOI	10.1109/TASLP.2017.2667880
Cilt	25

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Using eigenvoices and nearest-neighbors in HMM-based cross-lingual speaker adaptation with limited data

Author Sarfjoo, Seyyed Saeed, Demiroğlu, Cenk, King, S.

Publication Date 2017-04

Publication Place - IEEE

Subject Cross lingual speaker adaptation, Statistical speech synthesis, Speaker adaptation, Nearest neighbour

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2329-9304

Record ID ee9f3091-c0c9-4a59-92e6-4caaef7adf64

Library Location Electrical & Electronics Engineering

Date 2017-04

Notes European Commission ; TUBITAK

Sample Text Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.

DOI 10.1109/TASLP.2017.2667880

Cilt 25