Hypothesizing of words for isolated and connected word recognition systems based on phonem preclassification

Recognition of isolated or connected spoken words or sentences including a large vocabulary results in a great amount of classification expenditure. Reducing this expense by hypothesizing the words embedded in the speech signal is the goal of the hypothesizing process proposed in this paper. The process bases on the acoustic sound patterns and is accomplished by preclassification of significant phonems such as vowels and voiced consonants. The sequence of these phonems and their time distances within the speech signal is an appropriate criterion for hypothesizing and selecting of references from the lexicon. It is shown that this method can be applied successfully to isolated and connected word recognition on word and subword level reducing the classification expenditure by a great amount (120 to 2860 for isolated words). Results of the hypothesizing efficiency are presented for a 5000 word German vocabulary most frequently used. The hypothesizing process is supported successfully by a fast lexicon access method based on hash-coding and it proves to be robust even under failure in the prerecognized phonems.