Isolated word, speaker dependent recognition under the presence noise, based on an audio retrieval algorithm

With rapidly increasing storage and computational capacity, a common PC can store and index hundreds of hours of speech. This suggests that new approaches based on database techniques might be useful in speech recognition and speech indexing. This paper presents a first step in such a direction. The algorithm developed relies on an indexed single-speaker database. The database consists of spoken utterances transcribed into text. The waveforms of these utterances are converted off-line to binary symbols called fingerprints through a nonlinear frequency-domain transform. The fingerprints are associated with the transcribed text. Given the fingerprint of a new waveform, the best word match from the database can be retrieved. A 3255 word database is used as a test bed. All the words from this database are mixed with white noise and time-scale modified to provide test data. The database is queried with the fingerprint of the test words and the best match is retrieved. The results of the experiments conducted are promising, showing a 99.5% recognition rate for a 20 dB signal to noise ratio (SNR).