In this paper, Fuzzy C-Means clustering with Expectation Maximization-Gaussian Mixture Model based hybrid modeling algorithm is proposed for Continuous Tamil Speech Recognition. The speech sentences from various speakers are used for training and testing phase and objective measures are between the proposed and existing Continuous Speech Recognition algorithms. From the simulated results, it is...

This correspondence presents the first known results of complete recognition of continuous Mandarin speech for the Chinese language with very large vocabulary but very limited training data. Various acoustic and linguistic processing techniques were developed, and a prototype system of a continuous speech Mandarin dictation machine has been successfully implemented. The best recognition accurac...

Spelling recognition is an approach to enhance a speech recognizer’s ability to cope with incorrectly recognized words and out-of-vocabulary words. This paper presents a general framework for Thai speech recognition enhanced with spelling recognition. In order to implement Thai spelling recognition, Thai alphabets and their spelling methods are analyzed. Based on hidden Markov models, we propos...

This paper investigates the effectiveness of the DAEM (Deterministic Annealing EM) algorithm in acoustic modeling for speaker and speech recognition. Although the EM algorithm has been widely used to approximate the ML estimates, it has the problem of initialization dependence. To relax this problem, the DAEM algorithm has been proposed and confirmed the effectiveness in artificial small tasks....

A new scheme to represent phonological changes during continuous speech recognition is suggested. A phonological tag coupled with its morphological tag is designed to represent the conditions of Korean phonological changes. A pairwise language model of these morphological and phonological tags is implemented in Korean speech recognition system. Performance of the model is verified through the T...

Abstract The goal of this contribution is threefold: (1) to present the results of a comparative evaluation of different, academic and commercial, speech recognitions engines; (2) to study relative performances of Hidden Markov Model and hybrid technologies, as used in stateof-the-art systems; and (3) to study the impact of different linguistic resources, such as simple word spotting, statistic...

In mobile environments, user input and operation via voice are simple and effective. However, the speech recognition performance is highly influenced by ambient noise in the mobile environments, which may cause a significant deterioration of the performance, and there is a strong demand for improvement of the performance. In order to resolve these issues, we conducted research in two directions...