Abstract. In this paper, we focus on a novel NN/HMM architecture for continuous speech recognition. The architecture incorporates a neural feature extraction to gain more discriminative feature vectors for the underlying HMM system. The feature extraction can be chosen either linear or non-linear and can incorporate recurrent connections. With this hybrid system, that is an extension of a state...

We present our research on continuous speech recognition of the surface electromyographic signals that are generated by the human articulatory muscles. Previous research on electromyographic speech recognition was limited to isolated word recognition because it was very difficult to train phoneme-based acoustic models for the electromyographic speech recognizer. In this paper, we demonstrate ho...

This paper presents a new feature vector set for noisy speech recognition in autocorrelation domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In this paper we will use the autocorrelation domain as an appropriate candidate for robust feature extraction. In our approach, extraction of mel frequency cepstral coefficients (MFCC) of the speec...

As the population in developed countries is aging, larger numbers of people are at risk of developing dementia. In the near future there will be a need for timeand cost-efficient screening methods. Speech can be recorded and analyzed in this manner, and as speech and language are affected early on in the course of dementia, automatic speech processing can provide valuable support for such scree...

In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC)...

This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used to measure frontend feature extraction algorithms, using a defined HMM recognition back-end, or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by American English ...

The main task of speech recognition is to enable computer to understand human languages (Lawrence, 1999; Jingwei et al., 2006). This makes it possible that machine can communicate with human. Usually, speech recognition includes three parts: pre-processing, feature extraction and training (recognition) network. In this paper, the speech recognition system is described as Fig. 1. It consists of ...

This paper presents the use of neural net hierarchy for feature extraction in ASR. The recently proposed Bottle-Neck feature extraction is extended and used in hierarchical structures to enhance the discriminative property of the features. Although many ways of hierarchical classification/feature extraction have been proposed, we restricted ourselves to use the outputs of the first stage neural...

Pattern recognition have been a topic of active research during the 30 years, due to the high performance that these schemes presents, when they have been used in the solution of many practical problems in several fields of science, medicine and engineering. The efficiency of pattern recognition algorithms strongly depends in an accurate features extraction scheme that be able to represent the ...

This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used for the evaluation of front-end feature extraction algorithms using a defined HMM recognition back-end or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by America...