The Effect of SNR and GCI Perturbation on Speech Synthesis with Harmonic plus Noise Model
نویسندگان
چکیده
Harmonic plus noise model (HNM) divides the speech spectrum in two bands: harmonic and noise. As most of the non-periodic components are removed in harmonic part, it may be expected that HNM synthesis is less susceptible to additive noise in input speech. As HNM analysis/synthesis is performed at each glottal closure instant (GCI), errors in estimation of GCIs affect the quality of the synthesized speech. Effects of the amount of additive broadband noise in input speech and perturbation in GCIs on the synthesized speech quality with special reference to phoneme sets in Indian languages were studied. Synthesis results show that for SNR in the 2-10 dB range, the quality of synthesized speech is superior to that of the input speech. Investigations also show that the speech quality is very sensitive to positions of the GCIs. Perturbations above 8 % severely affect quality of the output speech.
منابع مشابه
Effect of GCI Perturbation on Speech Quality in Indian Languages
0-7803-7651-X/03/$17.00 © 2003 IEEE Abstract— Harmonic plus noise model (HNM) divides the speech spectrum into two bands: harmonic and noise. One is modeled with harmonics of the fundamental and the other is simulated using random noise. As HNM analysis/synthesis is performed at each glottal closure instant (GCI), errors in estimation of GCIs affect the quality of the synthesized speech. The ob...
متن کاملPerturbation in Gci's and Speech Quality for Pitch Synchronous Synthesis
In pitch synchronous speech synthesis the analysis/synthesis of the speech is done at each glottal closure instant (GCI). The errors in estimation of GCI's affect the quality of the synthesized speech. The effect of random perturbations in the GCI's, obtained from the speech and from glottal signal from an impedance electroglottograph using Childers and Hu's algorithm on the quality of speech s...
متن کاملSpeech enhancement based on hidden Markov model using sparse code shrinkage
This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...
متن کاملApproximate Kalman Filtering for the Harmonic plus Noise Model
We present a probabilistic description of the Harmonic plus Noise Model (HNM) for speech signals. This probabilistic formulation permits Maximum Likelihood (ML) parameter estimation and speech synthesis becomes a straightforward sampling from a distribution. It also permits development of a Kalman filter that tracks model parameters such as pitch, harmonic amplitudes, and autoregressive coeffic...
متن کاملSpeech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering
Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...
متن کامل