Speech synthesis with prosodic phrase boundary information

In spite of difficulty in defining the syllable unequivocally, and controversy over its role in theories of spoken and written language processing, the syllable is a potentially useful unit in several practical tasks which arise in computational linguistics and speech technology. For instance, syllable structure might embody valuable information for building word models in automatic speech recognition, and concatenative speech synthesis might use syllables or demisyllables as basic units. In this paper, we first present an algorithm for determining syllable boundaries in the orthographic form of unknown words that works by analogical reasoning from a database or corpus of known syllabifications. We call this syllabification by analogy (SbA). It is similarly motivated to our existing pronunciation by analogy (PbA) which predicts pronunciations for unknown words (specified by their spellings) by inference from a dictionary of known word spellings and corresponding pronunciations. We show that including perfect (according to the corpus) syllable boundary information in the orthographic input can dramatically improve the performance of pronunciation by analogy of English words, but such information would not be available to a practical system. So we next investigate combining automatically-inferred syllabification and pronunciation in two different ways: the series model in which syllabification is followed sequentially by pronunciation generation; and the parallel model in which syllabification and pronunciation are simultaneously inferred. Unfortunately, neither improves performance over PbA without syllabification. Possible reasons for this failure are explored via an analysis of syllabification and pronunciation errors.

Download Full-text

Instruction Task for Malay Phrase Boundary Annotation

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.15.17517 ◽

2018 ◽

Vol 7 (3.15) ◽

pp. 137

Author(s):

Haslizatul Mohamed Hanum ◽

Nur Atiqah Sia Abdullah ◽

Zainab Abu Bakar

Keyword(s):

Rater Agreement ◽

Continuous Speech ◽

Phrase Boundary ◽

Prosodic Phrase

The paper presents a refined instruction task to assist evaluation of prosodic phrase (PPh) boundaries by naive listeners. The results from the perceptual experiments were compared to the boundaries produced by online automatic tagger. The Kappa evaluation shows the average of 85% on inter-rater agreement. More than 60% of the boundaries which are detected by the automatic tagger matched the reference boundaries, showing that the refined instruction task can be used to evaluate perception on phrase boundaries on continuous speech.

Download Full-text

Active learning for the prediction of prosodic phrase boundaries in Chinese speech synthesis systems using conditional random fields

2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) ◽

10.1109/snpd.2015.7176201 ◽

2015 ◽

Author(s):

Ziping Zhao ◽

Xirong Ma

Keyword(s):

Active Learning ◽

Random Fields ◽

Speech Synthesis ◽

Conditional Random Fields ◽

Prosodic Phrase

Download Full-text

Prediction of Korean Prosodic Phrase Boundary by Efficient Feature Selection in Machine Learning

2009 21st IEEE International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2009.121 ◽

2009 ◽

Cited By ~ 1

Author(s):

Minho Kim ◽

Youngim Jung ◽

Hyuk-Chul Kwon

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Phrase Boundary ◽

Prosodic Phrase

Download Full-text

Korean Prosody Phrase Boundary Prediction Model for Speech Synthesis Service in Smart Healthcare

Electronics ◽

10.3390/electronics10192371 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2371

Author(s):

Minho Kim ◽

Youngim Jung ◽

Hyuk-Chul Kwon

Keyword(s):

Deep Learning ◽

Speech Processing ◽

Speech Synthesis ◽

Prediction Models ◽

Hearing Impairments ◽

Medical Field ◽

Speech Interfaces ◽

Phrase Boundary ◽

Smart Healthcare ◽

Medical Documents

Speech processing technology has great potential in the medical field to provide beneficial solutions for both patients and doctors. Speech interfaces, represented by speech synthesis and speech recognition, can be used to transcribe medical documents, control medical devices, correct speech and hearing impairments, and assist the visually impaired. However, it is essential to predict prosody phrase boundaries for accurate natural speech synthesis. This study proposes a method to build a reliable learning corpus to train prosody boundary prediction models based on deep learning. In addition, we offer a way to generate a rule-based model that can predict the prosody boundary from the constructed corpus and use the result to train a deep learning-based model. As a result, we have built a coherent corpus, even though many workers have participated in its development. The estimated pairwise agreement of corpus annotations is between 0.7477 and 0.7916 and kappa coefficient (K) between 0.7057 and 0.7569. In addition, the deep learning-based model based on the rules obtained from the corpus showed a prediction accuracy of 78.57% for the three-level prosody phrase boundary, 87.33% for the two-level prosody phrase boundary.

Download Full-text