A study of noise robustness for speaker independent speech recognition method using phoneme similarity vector

As an input method for rapidly spreading small portable information devices, development of speaker independent speech recognition technology which can be embedded on a single DSP is now urgently requested. We have reported a speech recognition method using phoneme similarity vector as a feature vector, which is quite robust for reduction of precision of the feature parameter. We’ve also developed a recognition board with a single DSP, which works 100-word vocabulary using only the internal memory inside the DSP. [1][2] In this report, we propose a new technique which makes our recognition method more robust, where a newly introduced noise standard template together with traditional phoneme standard templates for calculating phoneme similarity vector realizes precise word-spotting. When the newly proposed noise robustness method was tested with 100 isolated word vocabulary speech of 50 subjects, recognition accuracy of 94.7% was obtained under various noisy environments.