Feature extraction for spectral continuity measures in concatenative speech synthesis

2006 ◽  
Author(s):  
Barry Kirkpatrick ◽  
Darragh O’Brien ◽  
Ronán Scaife
Keyword(s):  
Feature Extraction ◽  
Speech Synthesis ◽  
Spectral Continuity

The Feature Extraction Algorithm for the Production of Emotions in Text-to-Speech (TTS) System for an Indian Regional Language

2010 ◽  
pp. 17-30
Author(s):  
Jagadish S Kallimani ◽  
V. K Ananthashayana ◽  
Debjani Goswami
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
Speech Synthesis ◽  
Text To Speech ◽  
Everyday Objects ◽  
Regional Language ◽  
Extraction Algorithm ◽  
Processing Signal ◽  
Emotional Aspects ◽  
Text To Speech Synthesis

Text-to-speech synthesis is a complex combination of language processing, signal processing and computer science. Ubiquitous computing (ubicomp) is a post-desktop model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. Speech synthesis is the generation of synthesized speech from text. This chapter deals with the development of a Text to Speech (TTS) Synthesis system for an Indian regional language by considering Bengali as the language. This chapter highlights various methods which may be used for speech synthesis and also it provides an overview on the problems and difficulties in Bengali text to speech conversion. Variations in the prosody (speech parameters – volume, pitch, intonation, amplitude) of the speech yields the emotional aspects (anger, happy, normal), which are applied to our developed TTS system.

A Novel Data Independent Approach for Conversion of Hand Punched Kannada Braille Script to Text and Speech

2018 ◽  
Vol 18 (02) ◽  
pp. 1850010 ◽  
Author(s):  
T. Shreekanth ◽  
M. R. Deeksha ◽  
Karthikeya R. Kaushik
Keyword(s):  
Feature Extraction ◽  
Performance Evaluation ◽  
Natural Language ◽  
Speech Synthesis ◽  
Basic Unit ◽  
Text To Speech ◽  
Synthesis Technique ◽  
Visually Challenged ◽  
Minimal Work ◽  
Regional Languages

In society, there exists a gap in communication between the sighted community and the visually challenged people due to different scripts followed to read and write. To bridge this gap there is a need for a system that supports automatic conversion of Braille script to text and speech in the corresponding language. Optical Braille Recognition (OBR) system converts the hand-punched Braille characters into their equivalent natural language characters. The Text-to-Speech (TTS) system converts the recognized characters into audible speech using speech synthesis techniques. Existing literature reveals that OBR and TTS systems have been well established independently for English. There is a scope for development of OBR and TTS systems for regional languages. In spite of Kannada being one of the most widely spoken regional languages in India, minimal work has been done towards Kannada OBR and TTS. There is no system that directly converts Braille script to speech, therefore, this development of Kannada Braille to text and speech system is one of a kind. The acquired image is processed and feature extraction is performed using [Formula: see text]-means algorithm and heuristics to convert the Braille characters to Kannada script. The concatenation based speech synthesis technique employing phoneme as the basic unit is used to convert Kannada TTS using Festival TTS framework. Performance evaluation of the proposed system is done using Kannada Braille database developed independently, and the results obtained are found to be satisfactory when compared to existing methods in the literature.

Estimation of window coefficients for dynamic feature extraction for HMM-based speech synthesis

2011 ◽  
Author(s):  
Ling-Hui Chen ◽  
Yoshihiko Nankaku ◽  
Heiga Zen ◽  
Keiichi Tokuda ◽  
Zhen-Hua Ling ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speech Synthesis ◽  
Dynamic Feature

scholarly journals Integration of Spectral Feature Extraction and Modeling for HMM-Based Speech Synthesis

2014 ◽  
Vol E97.D (6) ◽  
pp. 1438-1448 ◽  
Author(s):  
Kazuhiro NAKAMURA ◽  
Kei HASHIMOTO ◽  
Yoshihiko NANKAKU ◽  
Keiichi TOKUDA
Keyword(s):  
Feature Extraction ◽  
Speech Synthesis ◽  
Spectral Feature

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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