Phonem-based isolated Turkish word recognition with subspace classifier

In this study, phoneme-based isolated Turkish word recognition with Common Vector Approach (CVA) has been performed. CVA has been used to classify phonemes. The phoneme sequence obtained from the classification is decoded into the word using redundant hash addressing (RHA). The phoneme-based speech recognition is more suitable than the word-based speech recognition for implementing applications that use different words in their dictionaries. For that reason, in this study the CVA is evaluated to see whether it could be used in phoneme-based word recognition or not. In the experimental study we obtained the word recognition rates 70-80% from randomly selected words in METU database. It might be possible to obtain higher recognition rates by improving the CVA and by using different word decoding techniques.