The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic (ASR), has received significant increase in attention science and industry, which caused or was by an equally improvement accuracy. Meanwhile, it entered consumer market with digital home assistants language interface being its most prominent application. Speech recorded is affected various...

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...

The goal of this research is to improve the performance of a speaker-independent Automatic Speech Recognition (ASR) system by using directly measured articulatory parameters in the training phase. This paper examines the need for a multi-channel/multi-speaker articulatory database and describes the design of such a database and the processes involved in its creation.

speech emotion can add more information to speech in comparison to available textual information. however, it will also lead to some problems in speech recognition process. in a previous study, we depicted the substantial changes of speech parameters caused by speech emotion. therefore, in order to improve emotional speech recognition rate, in a first step, the effects of emotion on speech par...