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Analysis of speaker similarity in the statistical speech synthesis systems using a hybrid approach

Title	Analysis of speaker similarity in the statistical speech synthesis systems using a hybrid approach
Author	Güner, Ekrem, Mohammadi, A., Demiroğlu, Cenk
Publication Date:	2012
Publication Place	- IEEE
Subject	Speaker recognition, Speech synthesis, Statistical analysis
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-4673-1068-0
Record ID	6707d5da-edb6-44ec-8719-dd8fce587dfc
Library Location	Electrical & Electronics Engineering
Date	2012
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	Statistical speech synthesis (SSS) approach has become one of the most popular and successful methods in the speech synthesis field. Smooth speech transitions, without the spurious errors that are observed in unit selection systems, can be generated with the SSS approach. However, a well-known issue with SSS is the lack of voice similarity to the target speaker. The issue arises both in speaker-dependent models and models that are adapted from average voices. Moreover, in speaker adaptation, similarity to the target speaker does not increase significantly after around one minute of adaptation data which potentially indicates inherent bottleneck(s) in the system. Here, we propose using the hybrid speech synthesis approach to understand the key factors behind the speaker similarity problem. To that end, we try to answer the following question: which segments and parameters of speech, if generated/synthesized better, would have a substantial improvement on speaker similarity? In this work, our hybrid methods are described and listening test results are presented and discussed.

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Özyeğin University

Analysis of speaker similarity in the statistical speech synthesis systems using a hybrid approach

Author Güner, Ekrem, Mohammadi, A., Demiroğlu, Cenk

Publication Date 2012

Publication Place - IEEE

Subject Speaker recognition, Speech synthesis, Statistical analysis

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-4673-1068-0

Record ID 6707d5da-edb6-44ec-8719-dd8fce587dfc

Library Location Electrical & Electronics Engineering

Date 2012

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text Statistical speech synthesis (SSS) approach has become one of the most popular and successful methods in the speech synthesis field. Smooth speech transitions, without the spurious errors that are observed in unit selection systems, can be generated with the SSS approach. However, a well-known issue with SSS is the lack of voice similarity to the target speaker. The issue arises both in speaker-dependent models and models that are adapted from average voices. Moreover, in speaker adaptation, similarity to the target speaker does not increase significantly after around one minute of adaptation data which potentially indicates inherent bottleneck(s) in the system. Here, we propose using the hybrid speech synthesis approach to understand the key factors behind the speaker similarity problem. To that end, we try to answer the following question: which segments and parameters of speech, if generated/synthesized better, would have a substantial improvement on speaker similarity? In this work, our hybrid methods are described and listening test results are presented and discussed.