Automatic Speech Recognition Using Template Model for Man-Machine Interface
This work addresses the problem of improving natural human-computer interaction through speech recognition, but it appears incremental as it uses well-known methods without novel advancements.
The paper tackled automatic speech recognition for man-machine interfaces by implementing feature extraction with Mel-Frequency Cepstral Coefficients (MFCC) and pattern matching using the Dynamic Time Warping (DTW) algorithm, but it did not report any concrete results or numbers.
Speech is a natural form of communication for human beings, and computers with the ability to understand speech and speak with a human voice are expected to contribute to the development of more natural man-machine interfaces. Computers with this kind of ability are gradually becoming a reality, through the evolution of speech recognition technologies. Speech is being an important mode of interaction with computers. In this paper Feature extraction is implemented using well-known Mel-Frequency Cepstral Coefficients (MFCC).Pattern matching is done using Dynamic time warping (DTW) algorithm.