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Transformation of formants for voice conversion using artificial neural networks

M. Narendranath, +3 more
- 01 Feb 1995 - 
- Vol. 16, Iss: 2, pp 207-216
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TLDR
A scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker using formants and a formant vocoder is proposed.
About: 
This article is published in Speech Communication.The article was published on 1995-02-01 and is currently open access. It has received 207 citations till now. The article focuses on the topics: Formant & Voice analysis.

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Journal ArticleDOI

Analysis and Synthesis of Formant Spaces of British, Australian, and American Accents

Qin Yan, +3 more
- 01 Feb 2007 - 
TL;DR: Comparative analysis of the formant spaces of three major accents of the English language, namely, British Received Pronunciation, General American, and Broad Australian, are modeled and compared and indicates that these accents are partly conveyed by the differences of theformants of vowels.
Posted Content

An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning

Berrak Sisman, +3 more
- 09 Aug 2020 - 
TL;DR: A comprehensive overview of the state-of-the-art of voice conversion techniques and their performance evaluation methods from the statistical approaches to deep learning, and discuss their promise and limitations can be found in this paper.
Proceedings ArticleDOI

Auditory-Based Wavelet Packet Filterbank for Speech Recognition Using Neural Network

R Gandhiraj, +1 more
TL;DR: Two quantitative models for signal processing in auditory system (i) Gamma Tone Filter Bank (GTFB) and (ii) Wavelet Packet (WP) as front- ends for robust speech recognition are described.
Proceedings ArticleDOI

Voice Conversion by Prosody and Vocal Tract Modification

K. Sreenivasa Rao, +1 more
TL;DR: The proposed methods modify the shape of the vocal tract system and the characteristics of the prosody according to the desired requirement by manipulating instants of significant excitation from the linear prediction residual of the speech signals.
Posted Content

Introduction to Voice Presentation Attack Detection and Recent Advances

Sahidullah, +6 more
- 04 Jan 2019 - 
TL;DR: In the last few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV) for ASV, including the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers as discussed by the authors.
References
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Journal ArticleDOI

Multilayer feedforward networks are universal approximators

Kurt Hornik, +2 more
- 01 Jul 1989 - 
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Book

Fundamentals of speech recognition

Lawrence R. Rabiner, +1 more
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
Journal ArticleDOI

Analysis, synthesis, and perception of voice quality variations among female and male talkers

Dennis H. Klatt, +1 more
- 01 Feb 1990 - 
TL;DR: Perceptual validation of the relative importance of acoustic cues for signaling a breathy voice quality has been accomplished using a new voicing source model for synthesis of more natural male and female voices.
Journal ArticleDOI

Speech analysis and synthesis by linear prediction of the speech wave.

B. S. Atal, +1 more
- 01 Jan 1970 - 
TL;DR: Application of this method for efficient transmission and storage of speech signals as well as procedures for determining other speechcharacteristics, such as formant frequencies and bandwidths, the spectral envelope, and the autocorrelation function, are discussed.
Proceedings ArticleDOI

Voice conversion through vector quantization

Masanobu Abe, +3 more
TL;DR: The authors propose a new voice conversion technique through vector quantization and spectrum mapping which makes it possible to precisely control voice individuality.
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Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

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- 01 Nov 2007 - 

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Frequently Asked Questions (8)
Q1. What contributions have the authors mentioned in the paper "Transformation of formants for voice conversion using artificial neural networks" ?

In this paper the authors propose a scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker. The scheme consists of a formant analysis phase, followed by a learning phase in which the implicit formant transformation is captured by a neural network. 

In this paper the authors train a neural network to learn a transformation function which can transform the speaker dependent parameters extracted from the speech of the source speaker to match with that of the target speaker. 

But in continuous speech, since the vocal tract changes its shape continuously, the extracted formants will have many transitions. 

Fant’s model (Fant, 1986) was used to excite the formant synthesizer for voiced frames and random noise for the case of unvoiced frames. 

The first three formants from these two corresponding steady voiced regions are used as a pair of input and output formant vectors to a neural network. 

prosodic modifications were incorporated in the excitation signal using PSOLA (Pitch Synchronous Overlap Add) technique and speech was synthesized using the transformed spectral parameters. 

In the present study suprasegmental features of the source speaker are retained, while using the transformed vocal tract parameters for synthesis. 

They are (1) identification of speaker characteristics or acquisition of speaker dependent knowledge in the analysis phase and (2) incorporation of the speaker specific knowledge while synthesis during the transformation phase. 

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How do I save a voice message from signal?

In this paper we propose a scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker.

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