Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMM-TTS system using CBR as a F0 estimator, analysing its performance objectively and subjectively. The experiments have been conducted o...

Current developments in artificial speech synthesis place more emphasis on spectral continuities and diverse prosodic effects. The trainable HMM-based speech synthesis method has generated more continuous spectral structure than unit selection method in recent study, but the pitch contour generated by HMM-based method trends to be over-smoothed and lacks syllable variance in Chinese. In this pa...

In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE  estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of  noise are assumed zero-mean Gaussian distribution. Furthermore, the MMS...

The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limitations in its ability to model speech. A variety of modifications to the standard HMM topology have been proposed to handle these problems. One approach is the factorial HMM. This paper introduces a new form of factori...

HMM-based parametric speech synthesis has recently become an alternative to the concatenative TTS approach, especially when low footprint and general speech domain are required. A majority of speech parameterization models used in state-ofthe art HMM TTS systems employ source-filter waveform synthesis schemes. Sinusoidal representation and waveform generation of speech is an alternative to the ...

In this paper, we propose a non-realtime speech bandwidth extension method using HMM-based speech recognition and HMM-based speech synthesis. In the proposed method, first, the phoneme-state sequence is estimated from the bandlimited speech signals using the speech recognition technique. Next, for estimating spectrum envelopes of lost high-frequency components, an HMM-based speech synthesis tec...

The performance of speech recognition systems can be significantly degraded if the speech spectrum is distorted. This includes situations such as the usage of an improper recording device, enhancement technique or speech coder. This paper presents a front-end compensation method called spectrally selective dithering aimed at reconstructing the spectral characteristics of nonlinearly distorted s...