Residual prediction

Proc. Interspeech, 2009

Speech generated by parametric synthesizers generally suffers from a typical buzziness, similar to what was encountered in old LPC-like vocoders. In order to alleviate this problem, a more suited modeling of the excitation should be adopted. For this, we hereby propose an adaptation of the Deterministic plus Stochastic Model (DSM) for the residual. In this model, the excitation is divided into two distinct spectral bands delimited by the maximum voiced frequency. The deterministic part concerns the low-frequency ...

2012

The typical problem in LPC-like vocoder is buzzing sound which is mainly due to the simple pulse train or noise excitation model. One way to improve it is to reconstruct the residual obtained from inverse filtering. So a new parametric representation of speech based on pitch-scaled analysis is proposed in this paper. Pitch-scaled analysis is used to extract the periodic spectrum of residual with half pitch period length. Then these periodic spectrums are de-correlated by principal component analysis (PCA) to reduce their dimension. Aperiodic measure is defined as the harmonic-to-noise ratio in the frequency domain where voicing cut-off frequency (VCO) is used to control the smoothness of aperiodicity. Periodic spectrum and aperiodic measure together with F0 are indicated as excitation parameters in the proposed LPC vocoder. Experimental results show that this proposed vocoder can get a mean opinion score (MOS) of 4.1 for a female voice before dimensionality reduction and keep the hi...

IEEE Journal of Selected Topics in Signal Processing, 2014

Statistical parametric text-to-speech synthesis is optimized for regular voices and may not create high quality output with speakers producing irregular phonation frequently. A number of excitation models have been proposed recently in the hidden Markov-model speech synthesis framework, but few of them deal with the occurrence of this phenomenon. The baseline system of this study is our previous residual codebook based excitation model, which uses frames of pitch-synchronous residuals. To model the irregular voice typically occurring in phrase boundaries or sentence endings, two alternative extensions are proposed. The first, rule-based method applies pitch halving, amplitude scaling of residual periods with random factors and spectral distortion. The second, data-driven approach uses a corpus of residuals extracted from irregularly phonated vowels and unit selection is applied during synthesis. In perception tests of short speech segments, both methods have been found to improve the baseline excitation in preference and similarity to the original speaker. An acoustic experiment has shown that both methods can synthesize irregular voice that is close to original irregular phonation in terms of open quotient. The proposed methods may contribute to building natural, expressive and personalized speech synthesis systems.

2011

Abstract In hidden Markov model-based speech synthesis, speech is typically parameterized using source-filter decomposition. A widely used analysis/synthesis framework, STRAIGHT, decomposes the speech waveform into a framewise spectral envelope and a mixed mode excitation signal. Inclusion of an aperiodicity measure in the model enables synthesis also for signals that are not purely voiced or unvoiced.

2009

In this work, accurate spectral envelope estimation is applied to Voice Conversion in order to achieve High-Quality timbre conversion. True-Envelope based estimators allow model order selection leading to an adaptation of the spectral features to the characteristics of the speaker. Optimal residual signals can also be computed following a local adaptation of the model order in terms of the F0. A new perceptual criteria is proposed to measure the impact of the spectral conversion error. The proposed envelope models show improved spectral conversion performance as well as increased converted-speech quality when compared to Linear Prediction.

IEEE Transactions on Audio, Speech and Language Processing, 2012

A drawback of many voice conversion algorithms is that they rely on linear models and/or require a lot of tuning. In addition, many of them ignore the inherent time-dependency between speech features.

Eleventh Annual Conference of the …, 2010

Statistical methods for voice conversion are usually based on a single model selected in order to represent a tradeoff between goodness of fit and complexity. In this paper we assume that the best model may change over time, depending on the source acoustic features. We present a new method for spectral voice conversion 1 called Dynamic Model Selection (DMS), in which a set of potential best models with increasing complexity -including a mixture of Gaussian and probabilistic principal component analyzers -are considered during the conversion of a source speech signal into a target speech signal. This set is built during the learning phase, according to the Bayes information criterion (BIC). During the conversion, the best model is dynamically selected among the models in the set, according to the acoustical features of each source frame. Subjective tests show that the method improves the conversion in terms of proximity to the target and quality.

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009

This paper proposes a method to improve the quality delivered by statistical parametric speech synthesizers. For this, we use a codebook of pitch-synchronous residual frames, so as to construct a more realistic source signal. First a limited codebook of typical excitations is built from some training database. During the synthesis part, HMMs are used to generate filter and source coefficients. The latter coefficients contain both the pitch and a compact representation of target residual frames. The source signal is obtained by concatenating excitation frames picked up from the codebook, based on a selection criterion and taking target residual coefficients as input. Subjective results show a relevant improvement compared to the basic technique.

IEEE Automatic Speech Recognition Workshop, Arden House, Harriman, NY, pp. 35-38., 1991

In this paper we present several algorithms for simultaneous estimation of the non-linear as well as the linear prediction parameters of speech signals. Our study shows that the non-linear models retain substantially more information when compared to linear-only models. Preliminary experiments on telephone quality speech data clearly and consistently indicate that there is a significant reduction in the prediction error when the bilinear prediction components are included along with the LPC part. The results in this paper may have significant effect on the performance accuracy of any speech recognition/synthesis/coding system that currently relies on linear prediction only.

… 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.