Learning to Retrieve Out-of-Vocabulary Words in Speech Recognition
Table 2: Keyword Importance and Ranks by D-CBOW2 model
['Target OOV PNs', 'Reference Transcription D-CBOW2', 'Reference Transcription ranks', 'Reference Transcription ranks', 'LVCSR Transcription D-CBOW2', 'LVCSR Transcription ranks', 'LVCSR Transcription ranks']
We have listed four Euronews video examples. The first column lists the target OOV PNs present in these videos. For comparison the ranks given by the D-CBOW2 and D-CBOW models are also shown. It can be seen that in the first two examples the D-CBOW2 model extracts relevant keywords and gives similar ranks as D-CBOW model. In the third example, the ranks given by D-CBOW2 models are better than that of D-CBOW as the context, influenced by the keywords, is more focused. But relying on few words can cause degradation when (a) the LVCSR transcription loses some of the interesting words and (b) the target OOV PNs cannot be discriminated with the available interesting words. This can be seen in the fourth example, where words kiev and parti were mis-recognised during LVCSR transcription. And even though the word cfa, in the LVCSR transcription, is a good descriptor of the topic of the input document it does lead to good ranks as it is not close to the target OOV PNs in the learned space. The DCBOW model on the other hand takes a decision based on average of the words in the LVCSR transcription and thus gets relatively better ranks.