this paper introduces a novel approach to improve performance of speech recognition systems using a combination of features obtained from speech reconstructed phase space (rps) and frequency domain analysis. by choosing an appropriate value for the dimension, reconstructed phase space is assured to be topologically equivalent to the dynamics of the speech production system, and could therefore ...

Recent developments in speech synthesis have produced systems capable of outcome intelligible speech, but now researchers strive to create models that more accurately mimic human voices. One such development is the incorporation of multiple linguistic styles in various languages and accents. HMM-based Speech Synthesis is of great interest to many researchers, due to its ability to produce sophi...

In this paper we present a speech-recognizer-based maximum-likelihood beamforming technique, that can be used both for signal enhancement and speaker separation. The presented techniques uses an HMM-based speech recognizer as a statistical model for the target signal to be enhanced or separated. The parameters of a filter-and-sum array processor are estimated to maximize the likelihood of the o...

This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widelyused statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional ...

This paper presents a robust speech recognition method based on the HMM composition for the noisy room acoustics distorted speech. The method realizes an improved user interface such as the user is not encumbered by microphone equipments. The proposed HMM composition is obtained by naturally extending the HMM composition method of an additive noise to that of the convolutional room acoustics di...

This paper describes a hidden Markov model (HMM) based speech synthesis system developed for the Blizzard Challenge 2011. In the Blizzard Challenge 2011, we focused on the training algorithm for HMM-based speech synthesis systems. To alleviate the local maxima problems in the maximum likelihood estimation, we apply the deterministic annealing expectation maximization (DAEM) algorithm for traini...

This paper describes a hidden Markov model (HMM) based visual synthesizer designed to assist persons with impairedhearing. This synthesizer builds on results in the area of audio-visual speech recognition. We describe how a correlation HMM can be used to integrate independent acoustic and visual HMMs for speech-to-visual synthesis. Our results show that an HMM correlating model can signi cantly...

The paper investigates the potential of HMM based synthesis to support the parameterisation of expressive speech in German. First, we review the assets of HMMs in the perspective of previous works in speech modelling and speech transformation. It is shown that HMMs define a flexible parametric model of the speech acoustics, which readily integrates several levels of speech modelling, such as di...