Rejection of out-of-vocabulary words using phoneme confidence likelihood

The rejection of unknown words is important in improving the performance of speech recognition. The anti-keyword model method can reject unknown words with high accuracy in a small vocabulary and specified task. Unfortunately, it is either inconvenient or impossible to apply if words in the vocabulary change frequently. We propose a new method for task independent rejection of unknown words, where a new phoneme confidence measure is used to verify partial utterances. It is used to verify each phoneme while locating candidates. Furthermore, the whole utterance is verified by a phonetic typewriter. This method can improve the accuracy of verification in each phoneme, and improve the speed of candidate search. Tests show that the proposed method improves the recognition rate by 4% compared to the conventional algorithm at equal error rates. Furthermore, a 3% improvement is obtained by training acoustic models with the MCE algorithm.