Large vocabulary speaker-independent speech recognition systems being capable of recognizing continuous speech based on hidden Markov models are today’s standard. This review introduces the fundamentals of speech and the underlying speech recognition problems. The three classical approaches, i.e., the acoustic-phonetic, the statistical (pattern) recognition and the artificial intelligence appro...

BAHL, L.R., JELINEK, F., and MERCER, R.L.: ‘A maximum likelihood approach to continuous speech recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 1983, PAMI-5, (2), pp. 179-190 KAIFU LEE, and HSIAO-WUEN HON : ‘Speaker-independent phone recognition using hidden Markov models’, IEEE Trans. Acoust. Speech Signal Process., 1989, ASSP-37, (1 l), pp. 1641-1648 BOURLARD, H., and WELLEKENS, c.J.: ‘...

-—— In speaker-independent speech recognition. the disadvantage of the most di行used technology (HMMs.or Hidden Markov models)is not only the need of m any m ore training sam ples,but also long train tim e requirem ent. This PaPer describes the use of Biom im etic pattern recognition(BPR)in recognizing some mandarin continuous speech in a speaker-independent m anner. A speech database was develo...

Efficient online detection of similar patterns under arbitrary time scaling of a given time sequence is a challenging problem in the real-time application field of time series data mining. Some methods based on sliding window have been proposed. Although their ideas are simple and easy to realize, their computational loads are very expensive. Therefore, model based methods are proposed. Recentl...

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve comple...

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 ...

A Conditional Random Field is a mathematical model for sequences that is similar in many ways to a Hidden Markov Model, but is discriminative rather than generative in nature. In this paper, we explore the application of the CRF model to ASR processing of discriminative phonetic features by building a system that performs first-pass phonetic recognition using discriminatively trained phonetic f...

Abstract— Despite many years of concentrated research, the performance gap between automatic speech recognition (ASR) and human speech recognition (HSR) remains large. Especially for Arabic language, research efforts are still limited in comparison with other languages such as English or Japanese. In this work, we have use two algorithms to implement a system of Automatic Recognition of isolate...

This study deals with the shape recognition problem using the Hidden Markov Model (HMM). In many pattern recognition applications, selection of the size and topology of the HMM is mostly done by heuristics or using trial and error methods. It is well known that as the number of states and the non-zero state transition increases, the complexity of the HMM training and recognition algorithms incr...