background: according to previous studies, most of the speech recognition disorders in older adults are the results of deficits in audibility and auditory temporal resolution. in this paper, the effect of ageing on timecompressed speech and auditory temporal resolution by word recognition in continuous and interrupted noise was studied. methods: a time-compressed speech test (tcst) was conducte...

 Background: According to previous studies, most of the speech recognition disorders in older adults are the results of deficits in audibility and auditory temporal resolution. In this paper, the effect of ageing on timecompressed speech and auditory temporal resolution by word recognition in continuous and interrupted noise was studied. Methods: A time-compressed speech test (TCST) w...

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...

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 ...

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....

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...