I trained unigram, bigram and trigram language models and tested them with the Pocketsphinx recognizer. The results were quite good, however, the decoding time of the models were weird.
Average Decoding Time and RTF
Unigram: 3.33s (RFT: 0.87)
Bigram: 2.71s (RTF:0.74)
Trigram: 2.58s (RTF:0.70)
Is the Unigram model supposed to be the fastest among the three models?
Best Regards.
Nhan.
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Is the Unigram model supposed to be the fastest among the three models?
It is not that simple.
Pocketsphinx uses complex speech recognition algorithm with different kinds of pruning and interoperation between multi-pass searches. So unigram model with less accuracy might require more time just because it can not prune hypothesis effectively.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hi,
I trained unigram, bigram and trigram language models and tested them with the Pocketsphinx recognizer. The results were quite good, however, the decoding time of the models were weird.
Average Decoding Time and RTF
Unigram: 3.33s (RFT: 0.87)
Bigram: 2.71s (RTF:0.74)
Trigram: 2.58s (RTF:0.70)
Is the Unigram model supposed to be the fastest among the three models?
Best Regards.
Nhan.
It is not that simple.
Pocketsphinx uses complex speech recognition algorithm with different kinds of pruning and interoperation between multi-pass searches. So unigram model with less accuracy might require more time just because it can not prune hypothesis effectively.