< Back
Posted: 18 Apr 2022 01:00

“Speech Synthesis” April 2022 — summary from Crossref and DOAJ

Brevi Assistant
Brevi Assistant

Business performance assistant

“Speech Synthesis” April 2022 — summary from Crossref and DOAJ main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.

Crossref - summary generated by Brevi Assistant

Computer system speech synthesis has reached a high degree of efficiency, with significantly innovative models of etymological framework, reduced error rates in text analysis, and high intelligibility in synthesis from phonemic input. A variety of alternate directions of present research target at the supreme goal of totally natural artificial speech. Speech recognition has progressed rapidly in the previous decade through such methods, and it is promised that their application in synthesis will create similar enhancements. The conversion of text to speech is viewed as an analysis of the input text to get a common underlying linguistic description, adhered to by a synthesis of the output speech waveform from this essential specification. The enunciation of private words in unrestricted text is determined by morphological evaluation or letter-to-sound conversion, followed by specification of the word-level stress shape. When the prosodic correlates have been calculated and the segmental sequence is put together, a full input appropriate for speech synthesis has been identified.

The term speech synthesis has been used for diverse technological techniques. In this paper, some of the techniques utilized to generate synthetic speech in a text-to-speech system are assessed, and some of the basic motivations for selecting one approach over one more are reviewed. It is essential to remember, nonetheless, that speech synthesis models are required not just for speech generation but to help us comprehend just how speech is produced, and even how articulation can clarify language structure.

Source texts:

DOAJ - summary generated by Brevi Assistant

TTS synthesis systems are extensively used across the globe to magnify the accessibility of details and to make it feasible for the handicapped to be involved straight with computer systems to obtain take advantage of this high technology change. Synthesis system that utilizes concatenative synthesis approach since this method has the capability to collaborate the tiny corpus of speech to generate all-natural and intelligible noise.

Speech synthesis, also known as text-to-speech, has drawn significantly more attention. To much better understand the research characteristics in the speech synthesis field, this paper first of all introduces the typical speech synthesis approaches and highlights the relevance of the acoustic modeling from the structure of the statistical parametric speech synthesis system. When voicing is not present, current research in text-to-speech synthesis has revealed the benefit of making use of a continuous pitch estimate; one that interpolates basic frequency also. Outcomes based upon goal and perceptual examinations show that the voice constructed with the proposed structure offers modern speech synthesis efficiency while outperforming the previous standard.

End-to-end neural network-based speech synthesis techniques have been established to synthesize and stand for speech in various prosodic design. Although the end-to-end techniques allow the transfer of a style with a single vector of design representation, it has been reported that the audio speaker similarity observed from the speech manufactured with hidden speaker-style is low.

This paper offers the perceptual experiments that were performed in order to confirm the method of transforming meaningful speech styles by making use of voice quality parameters modelling, in addition to the popular prosody, from a neutral style into a variety of meaningful ones. The major goal was to confirm the efficiency of VoQ in the enhancement of expressive synthetic speech with regard to speech high quality and style recognition.

This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.

Source texts:


The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents/contents.


© All rights reserved 2022 made by Brevi Technologies