Modelling acoustic parameters of prosody for read and acted‐speech synthesis

2008 ◽  
Vol 123 (5) ◽  
pp. 3177-3177
Author(s):  
Milan Rusko ◽  
Marián Trnka ◽  
Sakhia Darjaa ◽  
Richard Kováč ◽  
Juraj Hamar
Keyword(s):  
Speech Synthesis ◽  
Acoustic Parameters

An Investigational Approach for Vowels of the Salar Language Based on a Database of Speech Acoustic Parameters

2021 ◽  
Vol 20 (5) ◽  
pp. 1-10
Author(s):  
Jun Ma ◽  
Hongzhi Yu ◽  
Yan Xu ◽  
Kaiying Deng
Keyword(s):  
Speech Synthesis ◽  
Research Work ◽  
Acoustic Parameter ◽  
Frequency Parameter ◽  
National Language ◽  
Speech Acoustics ◽  
Acoustic Parameters ◽  
Average Value ◽  
Basic Parameters ◽  
Second Formant

According to relevant specifications, this article divides, marks, and extracts the acquired speech signals of the Salar language, and establishes the speech acoustic parameter database of the Salar language. Then, the vowels of the Salar language are analyzed and studied by using the parameter database. The vowel bitmap (average value at the beginning of words), the vowel bitmap (average value at the abdomen of words), the vowel bitmap (average value at the ending of words), and the vowel bitmap (average value) are obtained. Through the vowel bitmaps, we can observe the vowel in different positions of the word, the overall appearance of an obtuse triangle. The high vowel [i], [o], and low vowel [a] occupy three vertices, respectively. Among the three lines, [i] to [o] are the longest, [i] to [a] are the second longest, and [a] to [o] are the shortest. The lines between [a] to [o] and [a] and [i] are asymmetric. Combining with the vowel bitmap, the vowels were discretized, and the second formant (F2) frequency parameter was used as the coordinate of the X axis, and the first formant (F1) frequency was used as the coordinate of the Y axis to draw the region where the vowel was located, and then the vowel pattern was formed. These studies provide basic data and parameters for the future development of modern phonetics such as the database of Sarah language speech, speech recognition, and speech synthesis. It also provides the basic parameters of speech acoustics for the rare minority acoustic research work of the national language project.

Rule Invention in the Acquisition of Morphology Revisited

1988 ◽  
Vol 31 (3) ◽  
pp. 425-431 ◽  
Author(s):  
Stephen M. Camarata ◽  
Lisa Erwin
Keyword(s):  
Language Acquisition ◽  
Active Role ◽  
Acoustic Parameters ◽  
Impaired Child ◽  
Language Impaired ◽  
Intensity Parameters ◽  
Suprasegmental Features ◽  
Acoustic Analyses ◽  
Suprasegmental Cues

This paper presents a case study of a language-impaired child who signaled the distinction between English singular and plural using suprasegmental cues rather than the usual segmental form used within the parent language. Acoustic analyses performed within the first study in the paper revealed that the suprasegmental features used to maintain this distinction included various duration, fundamental frequency, and intensity parameters. Acoustic analyses Were also performed on a set of matched two- and four-item plural forms within a second study. The results of these analyses indicated that the same acoustic parameters were used to distinguish two-item plural forms from four-item plural forms. This case of linguistic creativity is offered as further evidence in support of the model of language acquisition that emphasizes the active role children take in the acquisition process. Additionally, the phonological, morphological, and psycholinguistic factors that may contribute to such rule invention are discussed.

Speech synthesis from natural models by hand and by algorithm

2009 ◽  
Author(s):  
Robert E. Remez ◽  
Kathryn R. Dubowski ◽  
Morgana L. Davids ◽  
Emily F. Thomas ◽  
Nina Paddu ◽  
...  
Keyword(s):  
Speech Synthesis

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

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.

Excess Thermo Acoustic Parameters in 1,4-Dioxane With 1-Pentanol Binary System

2012 ◽  
Vol 3 (6) ◽  
pp. 415-418
Author(s):  
Anil Kumar K ◽  
◽  
Dr Srinivasu Ch Dr Srinivasu Ch
Keyword(s):  
Binary System ◽  
Acoustic Parameters

THE INFLUENCE OF ROOM ACOUSTIC PARAMETERS ON THE PERCEPTION OF ROOM CHARACTERISTICS

Akustika ◽  
2020 ◽  
pp. 28
Author(s):  
Alicja Jasińska ◽  
Maurycy Kin
Keyword(s):  
Standard Deviation ◽  
Subjective Evaluation ◽  
Strength Parameter ◽  
Acoustic Parameters ◽  
Sound Sources ◽  
Different Types

The article presents the possibility of identification of rooms on the basis of binaural perception. Results of subjective evaluation were compared with the values of sound strength, G. A previously unknown sound term was introduced: the strength of spatial impression as the inverse of standard deviation of the results obtained. It turned out that the results presenting the sound strength parameter can be correlated with the subjective evaluation of the spatial impression, which is the size of the room. It can be helpful in the process of room identification, probably due to the reverberation impression in the room. Authors plan to continue the study with more rooms and different types of sound sources.

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

2017 ◽  
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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