Inverse Filtering Based Harmonic Plus Noise Excitation Model for HMM-Based Speech Synthesis
نویسندگان
چکیده
In this paper, a new Voicing Cut-Off Frequency (VCO) estimation method based on inverse filtering is presented. The spectrum of residual signal got from inverse filtering is split into sub-bands which are clustered into two classes by using K-means algorithm. And then, the Viterbi algorithm is used to search a smoothed VCO contour. Based on this new VCO estimation method, an adaptation of Harmonic Noise Model is also proposed to reconstruct the residual signal with both harmonic and noise components. The proposed excitation model can reduce the buzziness of speech generated by normal vocoders using simple pulse train, and has been integrated into a HMM-based speech synthesis system (HTS). The listening test showed that the HTS with our new method gives better quality of synthesized speech than the traditional HTS which only uses simple pulse train excitation model.
منابع مشابه
An excitation model for HMM-based speech synthesis based on residual modeling
This paper describes a trainable excitation approach to eliminate the unnaturalness of HMM-based speech synthesizers. During the waveform generation part, mixed excitation is constructed by state-dependent filtering of pulse trains and white noise sequences. In the training part, filters and pulse trains are jointly optimized through a procedure which resembles analysis-bysynthesis speech codin...
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 کاملHMM-based Finnish text-to-speech system utilizing glottal inverse filtering
This paper describes an HMM-based speech synthesis system that utilizes glottal inverse filtering for generating natural sounding synthetic speech. In the proposed system, speech is first parametrized into spectral and excitation features using a glottal inverse filtering based method. The parameters are fed into an HMM system for training and then generated from the trained HMM according to te...
متن کاملThe GlottHMM Entry for Blizzard Challenge 2011: Utilizing Source Unit Selection in HMM-Based Speech Synthesis for Improved Excitation Generation
This paper describes the GlottHMM speech synthesis system for Blizzard Challenge 2011. GlottHMM is a hidden Markov model (HMM) based speech synthesis system that utilizes glottal inverse filtering for separating the vocal tract and the glottal source from speech signal and models both components individually. In this year’s entry, stabilized weighted linear prediction (SWLP) is used to yield mo...
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 کامل