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- W202359724 abstract "Abstract We study lattice rescoring with knowledge scores for automaticspeech recognition. Frame-based log likelihoodratio is adopted asa score measure of the goodness-of-fit between a speech segmentand the knowledge sources. We evaluate our approach in two dif-ferent applications: phone recognition, and connected digit con-tinuous recognition. By incorporating knowledge scores obtainedfrom 15 attribute detectors for place and manner of articulation,we reduced phone error rate from 40.52% to 35.16% using mono-phone models. The error rate can be further reduced to 33.42% fortriphone models. The same lattice rescoring algorithm is extendedto connected digit recognition using the TIDIGITS database, andwithout using any digit-specific training data. We observed thedigit error rate can be effectively reduced to 4.03% from 4.54%which was obtained with the conventional Viterbi decoding algo-rithm with no knowledge scores.Index Terms: detection-based automatic speech recognition, lat-tice rescoring, domain-independent speech recognition." @default.
- W202359724 created "2016-06-24" @default.
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- W202359724 date "2006-09-17" @default.
- W202359724 modified "2023-09-28" @default.
- W202359724 title "A study on lattice rescoring with knowledge scores for automatic speech recognition" @default.
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- W202359724 doi "https://doi.org/10.21437/interspeech.2006-198" @default.
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