The individual cognitive science disciplines all have contributions to make to the understanding and modelling of human learning. Our previous research has explored unsupervised learning of phonology, morphology and low-level syntax, as well as basic noun, verb and preposition ontology and semantics, plus musical and speech prosody. Successful applications using a mix of supervised and unsuperv...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

This work reports our research efforts towards developing efficient equipment for the automatic acoustic recognition of insects. In particular, we discuss the characteristics of the acoustic patterns of a target insect family, namely the cricket family. To address the recognition problem we apply a feature extraction methodology that has been inspired by well documented tactics of speech proces...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

Speech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, and a learning approach is introduced, which is based on the statistical model to classify internal feelings of the utterance....

Recent advances in speech recognition technology have raised hopes about the development of practical spoken natural language interfaces. Embedding the speech recognition technology within a sophisticated dialog processing mechanism can overcome many of the traditional problems in spoken natural language systems. This paper presents a theory of natural language dialog that enables: (1) robust b...

Fast and holistic access to the patients’ clinical record is a major requirement of modern medical decision support systems (DSS). While electronic health records (EHRs) have replaced the traditional paper-based records in most healthcare organization, the data entry into these systems remains largely manual. Speech recognition technology promises substitution of the more convenient speech-base...

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

speech emotion recognition (ser) is a new and challenging research area with a wide range of applications in man-machine interactions. the aim of a ser system is to recognize human emotion by analyzing the acoustics of speech sound. in this study, we propose spectral pattern features (sps) and harmonic energy features (hes) for emotion recognition. these features extracted from the spectrogram ...

For many years, speech has been the most natural and efficient means of information exchange for human beings. With the advancement of technology and the prevalence of computer usage, the design and production of speech recognition systems have been considered by researchers. Among this, lip-reading techniques encountered with many challenges for speech recognition, that one of the challenges b...