A feature extraction method for speech waves and an algorithm for sentence recognition are studied. The feature extraction is based on an articulatory model constructed from the statistical analysis of X-ray data. The model holds implicitly the physiological constraints and made possible to estimate the state of the articulatory mechanism. The estimated articulatory parameters provide a set of ...

An efficient speech to text converter for mobile application is presented in this work. The prime motive is to formulate a system which would give optimum performance in terms of complexity, accuracy, delay and memory requirements for mobile environment. The speech to text converter consists of two stages namely front-end analysis and patte rn recognition. The front end analysis involves prepro...

Probabilistic Finite State Machines (PFSM) are used in feature Extraction, training and testing which are the most important steps in any speech recognition system. An important PFSM is the Hidden Markov Model which is dealt in this paper. This paper proposes a hardware architecture for the forward-backward algorithm as well as the Viterbi Algorithm used in speech recognition based on Hidden Ma...

This paper investigates the effects of low-bit rate coded speech on the performance of a fixedtext speaker recognition system, under mismatched coding conditions between enrollment and testing. Significant degradation of performance has been observed relative to matched conditions, where same coding is used. Two techniques have been proposed to overcome mismatch effects; a linear discriminative...

The majority of speech recognition systems today commonly use Hidden Markov Models (HMMs) as acoustic models in systems since they can powerfully train and map a speech utterance into a sequence of units. Such systems perform even better if the units employed are context-dependent and gender-dependent. Analogously, when HMM technology is applied to the problem of articulatory feature extraction...

RelAtive SpecTral Analysis-Perceptual Linear Prediction (RASTA-PLP) is the standard speech feature extraction method used at the International Computer Science Institute. There it has been used primarily in conjunction with a hybrid Artiicial Neural Network (ANN) and Hidden Markov Model (HMM) speech recognition system. This work explores the viability of the RASTA-PLP as a candidate feature ext...

Filter bank approach is commonly used in feature extraction phase of speech recognition (e.g. Mel frequency cepstral coefficients). Filter bank is applied for modification of magnitude spectrum according to physiological and psychological findings. However, since mechanism of human auditory system is not fully understood, the optimal filter bank parameters are not known. This work presents a me...

Mel Frequency Cepstrum Coefficients (MFCCs) are considered as a method of stationary/pseudo-stationary feature extraction. They work very well for the classification of speech and music signals. MFCCs have also been used to classify non-speech sounds for audio surveillance systems, even though MFCCs do not completely reflect the time-varying features of non-stationary non-speech signals. We int...

Speech recognition are becoming more and more useful nowadays. Various interactive speech aware applications are available in the market. Speech recognition systems are the efficient alternatives for such devices where typing becomes difficult. But they are usually meant for and executed on the traditional general-purpose computers. With growth in the needs for embedded computing and the demand...