Adaptive Latency for Part-of-Speech Tagging in Incremental Text-to-Speech Synthesis

It’s a basic work for Tibetan information processing to tag the Tibetan parts of speech,the results can be used in machine translation, speech synthesis and so on. By studying the Tibetan language grammar and the classification of Tibetan parts of speech, established the Tibetan parts of speech tagging sets, and tagged the corpus, used the CRFs to solve the problem that automatic tagging of Tibetan parts of speech, the experimental results show that in the closed test set, part-of-speech tagging accuracy is 94.2%, and in the opening set, the accuracy is 91.5%.

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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Lexical Rule and Lexicon Effect for Part of Speech Tagging Bahasa Madura

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer ◽

10.30812/matrik.v18i1.332 ◽

2018 ◽

Vol 18 (1) ◽

pp. 65-72

Author(s):

Nindian Puspa Dewi ◽

Ubaidi Ubaidi

Keyword(s):

Text Processing ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Speech Tagging ◽

Bahasa Indonesia

POS Tagging adalah dasar untuk pengembangan Text Processing suatu bahasa. Dalam penelitian ini kita meneliti pengaruh penggunaan lexicon dan perubahan morfologi kata dalam penentuan tagset yang tepat untuk suatu kata. Aturan dengan pendekatan morfologi kata seperti awalan, akhiran, dan sisipan biasa disebut sebagai lexical rule. Penelitian ini menerapkan lexical rule hasil learner dengan menggunakan algoritma Brill Tagger. Bahasa Madura adalah bahasa daerah yang digunakan di Pulau Madura dan beberapa pulau lainnya di Jawa Timur. Objek penelitian ini menggunakan Bahasa Madura yang memiliki banyak sekali variasi afiksasi dibandingkan dengan Bahasa Indonesia. Pada penelitian ini, lexicon selain digunakan untuk pencarian kata dasar Bahasa Madura juga digunakan sebagai salah satu tahap pemberian POS Tagging. Hasil ujicoba dengan menggunakan lexicon mencapai akurasi yaitu 86.61% sedangkan jika tidak menggunakan lexicon hanya mencapai akurasi 28.95 %. Dari sini dapat disimpulkan bahwa ternyata lexicon sangat berpengaruh terhadap POS Tagging.

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Mongolian part-of-speech tagging approach based on conditional random fields

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.02038 ◽

2010 ◽

Vol 30 (8) ◽

pp. 2038-2040

Author(s):

Yu-long YING ◽

Miao LI ◽

bala Wuda ◽

Hai ZHU

Keyword(s):

Random Fields ◽

Conditional Random Fields ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Speech Tagging

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Integrating Articulatory Information in Deep Learning-Based Text-to-Speech Synthesis

10.21437/interspeech.2017-1762 ◽

2017 ◽

Cited By ~ 1

Author(s):

Beiming Cao ◽

Myungjong Kim ◽

Jan van Santen ◽

Ted Mau ◽

Jun Wang

Keyword(s):

Deep Learning ◽

Speech Synthesis ◽

Text To Speech ◽

Text To Speech Synthesis

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Subset Selection, Adaptation, Gemination and Prosody Prediction for Amharic Text-to-Speech Synthesis

10.21437/ssw.2019-37 ◽

2019 ◽

Author(s):

Elshadai Tesfaye Biru ◽

Yishak Tofik Mohammed ◽

David Tofu ◽

Erica Cooper ◽

Julia Hirschberg

Keyword(s):

Speech Synthesis ◽

Subset Selection ◽

Text To Speech ◽

Text To Speech Synthesis ◽

Prosody Prediction

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