Isolated Speech Recognition and Its Transformation in Visual Signs

This paper proposes a precise approach of achieving a visual transformation of the isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with a systematic image display. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-frequency cepstral coefficients (MFCCs), Delta coefficients and Delta–Delta coefficients are extracted from the enhanced speech segment. Later on, the dynamic time warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a systematic sign. The system functionality is tested with an experimental setup and results are presented. An average isolated word recognition accuracy of 99% is achieved. The proposed approach has a potential to be employed in potential applications like visual arts, industrial and noisy environments, integration of people with impaired hearing, education, etc.