Parametric Formant Modelling and Transformation in Voice Conversion (original) (raw)
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Probability models of formant parameters for voice conversion
… Conference on Speech …, 2003
This paper explores the estimation and mapping of probability models of formant parameter vectors for voice conversion. The formant parameter vectors consist of the frequency, bandwidth and intensity of resonance at formants. Formant parameters are derived from the coefficients of a linear prediction (LP) model of speech. The formant distributions are modelled with phonemedependent two-dimensional hidden Markov models with state Gaussian mixture densities. The HMMs are subsequently used for re-estimation of the formant trajectories of speech. Two alternative methods are explored for voice morphing. The first is a non-uniform frequency warping method and the second is based on spectral mapping via rotation of the formant vectors of the source towards those of the target. Both methods transform all formant parameters (Frequency, Bandwidth and Intensity). In addition, the factors that affect the selection of the warping ratios for the mapping function are presented. Experimental evaluation of voice morphing examples is presented.
Voice conversion through transformation of spectral and intonation features
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004
This paper presents a voice conversion method based on transformation of the characteristic features of a source speaker towards a target. Voice characteristic features are grouped into two main categories: (a) the spectral features at formants and (b) the pitch and intonation patterns. Signal modelling and transformation methods for each group of voice features are outlined. The spectral features at formants are modelled using a set of two-dimensional phoneme-dependent HMMs. Subband frequency warping is used for spectrum transformation with the subbands centred on the estimates of the formant trajectories. The F0 contour is used for modelling the pitch and intonation patterns of speech. A PSOLA based method is employed for transformation of pitch, intonation patterns and speaking rate. The experiments present illustrations and perceptual evaluations of the results of transformations of the various voice features.
Transformation of speaker characteristics for voice conversion
2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721), 2003
This paper presents a voice conversion method based on analysis and transformation of the characteristics that define a speaker's voice. Voice characteristic features are grouped into three main categories: (a) the spectral features at formants, (b) the pitch and intonation pattern and (c) the glottal pulse shape. Modelling and transformation methods of each group of voice features are outlined. The spectral features at formants are modelled using a two-dimensional phoneme-dependent HMMs. Subband frequency warping is used for spectrum transformation where the subbands are centred on estimates of formant trajectories. The F0 contour, extracted from autocorrelation-based pitchmarks, is used for modelling the pitch and intonation patterns of speech. A PSOLA based method is used for transformation of pitch, intonation patterns and speaking rate. Finally a method based on de-convolution of the vocal tract is used for modelling and mapping of the glottal pulse. The experimental results present illustrations of transformations of the various characteristics and perceptual evaluations.
Voice conversion based on parameter transformation
1998
This paper describes a voice conversion system based on parameter transformation [1]. Voice conversion is the process of making one person's voice "source" sound like another person's voice "target"[2]. We will present a voice conversion scheme consisting of three stages. First an analysis is performed on the natural speech to obtain the acoustical parameters. These parameters will be voiced and unvoiced regions, the glottal source model, pitch, energy, formants and bandwidths. Once these parameters have been obtained for two different speakers they are transformed using linear functions. Finally the transformed parameters are synthesized by means of a formant synthesizer. Experiments will show that this scheme is effective in transforming the speaker individuality. It will also be shown that the transformation can not be unique from one speaker to another but it has to be divided in several functions each to transform a certain part of the speech signal. Segmentation based on spectral stability will divide the sentence into parts, for each segment a transformation function will be applied.
Voice morphing based on spectral features and prosodic modification
17th IEEE International Multi Topic Conference 2014, 2014
This paper is aimed at morphing the speech uttered by a source speaker in a manner that it seems to be spoken by another target speakera new identity is given while preserving the original content. The proposed method transforms the vocal tract parameters and glottal excitation of the source speaker into target speaker's acoustic characteristics. It relates to the development of appropriate vocal tract models that can capture information specific to the speaker and estimate the model parameters that closely relate to the model of the target speaker. It detects the pitch, separates the glottal excitation and vocal tract spectral features. The glottal excitation of the source is taken, voice/un-voice decision is made, the prosody information is found, PSOLA is used to modify the pitch, the spectral features are found, and finally speech is modified using target spectral features and prosody. The subjective experiment shows that the proposed method improves the quality of conversion and contains the original vocal and glottal characteristics of the target speaker.
Mapping Articulatory-Features to Vocal-Tract Parameters for Voice Conversion
IEICE Transactions on Information and Systems, 2014
In this paper, we propose voice conversion (VC) based on articulatory features (AF) to vocal-tract parameters (VTP) mapping. An artificial neural network (ANN) is applied to map AF to VTP and to convert a speaker's voice to a target-speaker's voice. The proposed system is not only text-independent VC, in which it does not need parallel utterances between source and target-speakers, but can also be used for an arbitrary sourcespeaker. This means that our approach does not require source-speaker data to build the VC model. We are also focusing on a small number of target-speaker training data. For comparison, a baseline system based on Gaussian mixture model (GMM) approach is conducted. The experimental results for a small number of training data show that the converted voice of our approach is intelligible and has speaker individuality of the targetspeaker.
International Journal of Speech Technology, 2012
This paper presents a framework, named Hidden Markov Model-Weighted Deviation Linear Transformation (HMM-WDLT), for performing voice conversion based on the Harmonic + Noise Model (HNM). The HMM-WDLT achieves the lowest average spectral distortion in a comparative study of spectral conversion. The problem with broader formant bandwidths can be remedied by a weighting constraint and ordering check with the minimum clearance estimated from the HMM-WDLT. By jointly exploiting the dynamic time warping (DTW) and the HMM-WDLT, the conversion in duration is also feasible. Moreover, the HMM-WDLT plays a part in the conversion of excitationrelated parameters such as the fundamental frequency, maximum voiced frequency, and harmonic magnitudes for critical bands below 2.7 kHz. The ability of modifying the pitch and duration concurrently allows the HMM-WDLT to carry out the prosody conversion. Listening tests reveal that the converted speech successfully catches the speaker's individuality with satisfactory quality.
Including dynamic and phonetic information in voice conversion systems
Proc. of the ICSLP'04, 2004
Voice Conversion (VC) systems modify a speaker voice (source speaker) to be perceived as if another speaker (target speaker) had uttered it. Previous published VC approaches using Gaussian Mixture Models [1] performs the conversion in a frame-by-frame basis using only spectral information. In this paper, two new approaches are studied in order to extend the GMM-based VC systems. First, dynamic information is used to build the speaker acoustic model. So, the transformation is carried out according to sequences of frames. Then, phonetic information is introduced in the training of the VC system. Objective and perceptual results compare the performance of the proposed systems.
Prosody Modifications for Voice Conversion
2013
Generally defined, speech modification is the process of changing certain perceptual properties of speech while leaving other properties unchanged. Among the many types of speech information that may be altered are rate of articulation, pitch and formant characteristics.Modifying the speech parameters like pitch, duration and strength of excitation by desired factor is termed as prosody modification. In this thesis prosody modifications for voice conversion framework are presented.Among all the speech modifications for prosody two things are important firstly modification of duartion and pauses (Time scale modification) in a speech utterance and secondly modification of the pitch(pitch scale modification).Prosody modification involves changing the pitch and duration of speech without affecting the message and naturalness.In this work time scale and pitch scale modifications of speech are discussed using two methods Time Domain Pitch Synchronous Overlapped-Add (TD-PSOLA) and epoch based approach.In order to apply desired speech modifications TD-PSOLA discussed in this thesis works directly on speech in time domian although there are many variations of TD-PSOLA.The epoch based approach involves modifications of LP-residual. Among the various perceptual properties of speech pitch contour plays a key role which defines the intonation patterns of speaker.Prosody modifications of speech in voice conversion framework involve modification of source pitch contour as per the pitch contour of target.In a voice conversion framework it requires prediction of target pitch contour. Mean/ variance method for pitch contour prediction is explored. Sinusoidal modeling has been successfully applied to a broad range of speech processing problems. It offers advantages over linear predictive modeling and the short-time Fourier transform for speech analysis/ synthesis and modification. The parameter estimation of sinusoidal modeling which permits flexible time and frequency scale voice modifications is presented. Speech synthesis using three models sinusoidal, harmonic and harmonic-plus-residual is discussed. vi