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Low memory hybrid statistical/unit selective MKS system for additive languages

Title	Low memory hybrid statistical/unit selective MKS system for additive languages
Author	Guner, Ekrem, Demiroğlu, Cenk
Publication Date:	2012
Publication Place	- IEEE
Subject	Hidden Markov models, Natural language processing, Speech intelligibility, Speech synthesis
Type	Document
Language	Turkish
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2165-0608
Record ID	17f5f720-0ab9-4062-8748-bc50bb04386a
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	The HMM-based TTS (HTS) approach has been increasingly getting more attention from the TTS research community. One of the advantage is the lack of spurious errors that are observed in the unit selection scheme. Another advantage of the HTS system is the small memory footprint requirement which makes it attractive for embedded devices. Here, we propose a novel hybrid statistical unit selection TTS system for agglutinative languages that aims at improving the quality of the baseline HTS system while keeping the memory footprint small. The intelligibility and quality scores of the baseline system are comparable to the MOS scores of English reported in the Blizzard Challenge tests. Listeners preferred the hybrid system over the baseline system in the A/B preference tests.
DOI	10.1109/SIU.2012.6204745

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Özyeğin University

Low memory hybrid statistical/unit selective MKS system for additive languages

Author Guner, Ekrem, Demiroğlu, Cenk

Publication Date 2012

Publication Place - IEEE

Subject Hidden Markov models, Natural language processing, Speech intelligibility, Speech synthesis

Type Document

Language Turkish

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2165-0608

Record ID 17f5f720-0ab9-4062-8748-bc50bb04386a

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 The HMM-based TTS (HTS) approach has been increasingly getting more attention from the TTS research community. One of the advantage is the lack of spurious errors that are observed in the unit selection scheme. Another advantage of the HTS system is the small memory footprint requirement which makes it attractive for embedded devices. Here, we propose a novel hybrid statistical unit selection TTS system for agglutinative languages that aims at improving the quality of the baseline HTS system while keeping the memory footprint small. The intelligibility and quality scores of the baseline system are comparable to the MOS scores of English reported in the Blizzard Challenge tests. Listeners preferred the hybrid system over the baseline system in the A/B preference tests.

DOI 10.1109/SIU.2012.6204745