DATA-DRIVEN PHRASING FOR SPEECH SYNTHESIS IN LOW-RESOURCE LANGUAGES

被引：0

作者：

Parlikar, Alok ^{[1
]}

Black, Alan W. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

关键词：

Speech Synthesis; Phrase Break Prediction; Low Resource Languages;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We present an approach to build phrase break prediction models when synthesizing text in low resource languages. This method allows building models without depending on the availability of part of speech taggers, or corpus with hand annotated breaks. We use the same speech data used for building a synthetic voice, to deduce acoustic phrase breaks. We perform unsupervised part of speech induction over a small text corpus in the language at hand. We use these tags and train a grammar based phrasing model. In this paper, we show results for the languages: English, Portuguese and Marathi, which suggest that we can quickly build very reasonable phrasing models for new languages using very little data.

引用

页码：4013 / 4016

页数：4

共 50 条

[21] Automatic Speech Transcription for Low-Resource Languages - The Case of Yoloxfochitl Mixtec (Mexico)
Mitral, Vikramjit
Katholl, Andreas
Amith, Jonathan D.
Castillo Garcia, Rey
[J]. 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES, 2016, : 3076 - 3080
[22] END-TO-END SPEECH RECOGNITION AND KEYWORD SEARCH ON LOW-RESOURCE LANGUAGES
Rosenberg, Andrew
Audhkhasi, Kartik
Sethy, Abhinav
Ramabhadran, Bhuvana
Picheny, Michael
[J]. 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 5280 - 5284
[23] SUPERVISED AND UNSUPERVISED ACTIVE LEARNING FOR AUTOMATIC SPEECH RECOGNITION OF LOW-RESOURCE LANGUAGES
Syed, Ali Raza
Rosenberg, Andrew
Kislal, Ellen
[J]. 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 5320 - 5324
[24] A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages
Cardenas, Ronald
Lin, Ying
Ji, Heng
May, Jonathan
[J]. 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, 2019, : 2428 - 2439
[25] Voice Activation for Low-Resource Languages
Kolesau, Aliaksei
Sesok, Dmitrij
[J]. APPLIED SCIENCES-BASEL, 2021, 11 (14):
[26] Speech-to-speech Low-resource Translation
Liu, Hsiao-Chuan
Day, Min-Yuh
Wang, Chih-Chien
[J]. 2023 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE, IRI, 2023, : 91 - 95
[27] MIXSPEECH: DATA AUGMENTATION FOR LOW-RESOURCE AUTOMATIC SPEECH RECOGNITION
Meng, Linghui
Xu, Jin
Tan, Xu
Wang, Jindong
Qin, Tao
Xu, Bo
[J]. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 7008 - 7012
[28] EXPLORING EFFECTIVE DATA UTILIZATION FOR LOW-RESOURCE SPEECH RECOGNITION
Zhou, Zhikai
Wang, Wei
Zhang, Wangyou
Qian, Yanmin
[J]. 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 8192 - 8196
[29] Incident-Driven Machine Translation and Name Tagging for Low-resource Languages
Hermjakob, Ulf
Li, Qiang
Marcu, Daniel
May, Jonathan
Mielke, Sebastian J.
Pourdamghani, Nima
Pust, Michael
Shi, Xing
Knight, Kevin
Levinboim, Tomer
Murray, Kenton
Chiang, David
Zhang, Boliang
Pan, Xiaoman
Lu, Di
Lin, Ying
Ji, Heng
[J]. MACHINE TRANSLATION, 2018, 32 (1-2) : 59 - 89
[30] A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
Vania, Clara
Kementchedjhieva, Yova
Sogaard, Anders
Lopez, Adam
[J]. 2019 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND THE 9TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (EMNLP-IJCNLP 2019): PROCEEDINGS OF THE CONFERENCE, 2019, : 1105 - 1116

← 1 2 3 4 5 →