Bioinformatics and Handwriting/Speech Reconition: Uncoventional Applications of Similarity Search Tools
Author(s)Jensen, Kyle; Stephanopoulos, Gregory
This work introduces two unconventional applications for sequence alignment algorithms outside the domain of bioinformatics: handwriting recognition and speech recognition. In each application we treated data samples, such as the path of a and written pen stroke, as a protein sequence and use the FastA sequence alignment tool to classify unknown data samples, such as a written character. That is, we handle the handwriting and speech recognition problems like the protein annotation problem: given a sequence of unknown function, we annotate the sequence via sequence alignment. This approach achieves classification rates of 99.65% and 93.84% for the handwriting and speech recognition respectively. In addition, we provide a framework for applying sequence alignment to a variety of other non–traditional problems.
Molecular Engineering of Biological and Chemical Systems (MEBCS)
Machine learning, bioinformatics, amino acids, protein sequences, sequence alignment, FastA, voice, dynamic programming, handwriting