We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities. Because of its adaptive nature, Bayesian learning serves as a unified approach for...

Today speech recognition is requested not only to decode utterances into transcriptions, but also to determine the reliabilities of the result, by Utterance Verification (UV). With the conventional HMM, the measure of reliabilities can not be determined directly by the likelihoods of models. Whereas, Modified Segmental Probability Model (MSPM), suggested in this paper, with its normalized likel...

A theoretical and experimental analysis of a simple multilevel segmental HMM is presented in which the relationship between symbolic (phonetic) and surface (acoustic) representations of speech is regulated by an intermediate (articulatory) layer, where speech dynamics are modeled using linear trajectories. Three formant-based parameterizations and measured articulatory positions are considered ...

Introduction: Research into segment models (SMs) for automatic speech recognition is motivated by limitations of conventional hidden Markov models (HMMs). While HMMs associate states with individual feature vectors, SMs associate states with sequences of vectors (segments) [1], or variable duration acoustic features [2], thereby allowing important static and dynamic structure to be modelled. Gl...

This paper presents an initial study to perform Iarge-vocabuIary continuous Mandarin speech recognition based on a Segmental Probability Model(SPM) approach. SPM was first proposed for recognition of isolated Mandarin syllables, in which every syllable must be equally segmented before recognition. Therefore, A concatenated syllable matching algorithm in place of the conventional Viterbi search ...

This paper introduces an unsupervised prosody labeling method for preparing a large speech corpus used in developing a Mandarin Text-to-Speech system. Adopting a four-layer prosody hierarchy, the proposed method performs an unsupervised segmental clustering that iteratively segments spoken utterances into strings of prosodic constituents and models the patterns of the segmented prosodic constit...

In this paper experiments are presented to apply Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) for training mixture density hidden Markov models (HMMs) in automatic speech recognition. The decoding of spoken words into text is made using speaker dependent, but vocabulary and context independent phoneme HMMs. Each HMM has a set of states and the output density of each state is...

A prosodic Hidden Markov model (HMM) based modality recognizer has been developed, which, after supra-segmental acoustic pre-processing, can perform clause and sentence boundary detection and modality (sentence type) recognition. This modality recognizer is adapted to carry out automatic evaluation of the intonation of the produced utterances in a speech training system for hearing-impaired per...

Detection of Parkinson’s disease (PD) at an early stage is necessary for its treatment. The commonly used methods available in the literature use observation of certain symptoms such as Tremor, Loss of Smell and Troubled Sleeping, Moving or Walking. The motion pattern in this disease can be characterized by a spatio-temporal phenomenon that signifies gait recognition as reported in the literatu...