Speech synthesis has been applied in many kinds of practical applications. Currently, state-of-the-art speech synthesis uses statistical methods based on hidden Markov model (HMM). Speech synthesized by statistical methods can be considered over-smooth caused by the averaging in statistical processing. In the literature, there have been many studies attempting to solve over-smoothness in speech...

This paper presents the development of a hidden Markov model (HMM)-based Malay text-to-speech (TTS) system. To our knowledge, this is the first report on the development of the HMM-based speech synthesis system for the Malay language. In this paper, We first discuss the Malay speech characteristics, specifically, on Malay phonological system and syllable structure. In the Malay phonological sys...

In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text an...

Hidden Markov model (HMM) -based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervi...

This paper describes an approach to voice characteristics conversion for HMM-based text-to-speech synthesis system by using speaker interpolation. An HMM interpolation technique is derived from a probabilistic distance measure for HMMs, and used to synthesize speech with untrained speaker’s characteristics by interpolating HMM parameters among some representative speakers’ HMM sets. The result ...

This paper presents an ‘elitist approach’ for extracting automatically well-realized speech sounds with high confidence. The elitist approach uses a speech recognition system based on Hidden Markov Models (HMM). The HMM are trained on speech sounds which are systematically well-detected in an iterative procedure. The results show that, by using the HMM models defined in the training phase, the ...

in this paper, genetic programming is applied for quality improvement of noisy speech signal. therefore, a system including both spectral subtraction and genetic programming is implemented for speech enhancement. in the proposed method, first noise is reduced by spectral subtraction. in the next step, genetic programming trees are trained for more enhancement of noisy signal by mapping the sign...