the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...

A segmental HMM is a HMM whose states are associated with sequences of acoustic feature vectors (or segments), rather than individual vectors. By treating segments as homogeneous units it is possible, for example, to develop better models of speech dynamics. This paper begins by describing a type of segmental HMM in which the relationship between the state and acoustic level descriptions of a s...

Hybrid systems which integrate the deep neural network (DNN) and hidden Markov model (HMM) have recently achieved remarkable performance in many large vocabulary speech recognition tasks. These systems, however, remain to rely on the HMM and assume the acoustic scores for the (windowed) frames are independent given the state, suffering from the same difficulty as in the previous GMM-HMM systems...

In this paper, a method for prosodic break modelling based on segmental-HMMs and Dempster-Shafer fusion for speech synthesis is presented, and the relative importance of linguistic and metric constraints in prosodic break modelling is assessed . A context-dependent segmental-HMM is used to explicitly model the linguistic and the metric constraints. Dempster-Shafer fusion is used to balance the ...

In this paper a theoretical framework for Bayesian adaptive learning of discrete HMM and semi continuous one with Gaussian mixture state observation densities is presented Corre sponding to the well known Baum Welch and segmental k means algorithms respectively for HMM training formulations of MAP maximum a posteriori and segmental MAP estima tion of HMM parameters are developed Furthermore a c...

For robust speech recognition in noisy environments, various methods have been studied. In this paper, we apply parallel model combination (PMC) for segmental unit input HMM to recognize corrupted speech in additive noise. Since several successive frames are combined and treated as an input vector in segmental unit input modeling, the increased dimension of vector degrades the precision in esti...

In this paper, a novel segmental Hidden Markov Model (HMM) is proposed. The model is based on a modified emission density where additional statistical dependencies between subsequent frames of the speech signal are considered. In the following we derive an effective search strategy for the modified statistical model. Further an approach to parameter reduction is introduced. Experiments were car...

In this paper, the reduction method of number of parameters in the segmental-feature HMM (SFHMM) can be considered. It is reported that the SFHMM shows better results than conventional HMM in the previous studies. However, its number of parameters is greater than that of HMM. Therefore, there is a need for new approach that reduces the number of parameters. The trajectories are used for the aco...

In this paper, a parametric trajectory segment model (PTSM) with segmental inner time warping is proposed to improve the recognition accuracy of large vocabulary continuous speech recognition(LVCSR). The proposed PTSM utilizes the state boundary information provided by HMM system during decoding to do segmental inner time warping. Good alignment between different length realizations of a same p...

In this paper, we present investigations on using segmental phonetic features in an N-best solutions post processing of an HMM based ASR system. These phonetic features are extracted by means of neural-fuzzy networks. Specialized neural-fuzzy networks are defined to recognize specific phonetic features (consonant/vowel, voiced/unvoiced, ...). Each of these neural networks furnishes a segmental ...