Title :
LVCSR log-likelihood ratio scoring for keyword spotting
Author :
Weintraub, Mitchel
Author_Institution :
Speech Technol. & Res. Program, SRI Int., Menlo Park, CA, USA
Abstract :
A new scoring algorithm has been developed for generating wordspotting hypotheses and their associated scores. This technique uses a large-vocabulary continuous speech recognition (LVCSR) system to generate the N-best answers along with their Viterbi alignments. The score for a putative hit is computed by summing the likelihoods for all hypotheses that contain the keyword normalized by dividing by the sum of all hypothesis likelihoods in the N-best list. Using a test set of conversational speech from Switchboard Credit Card conversations, we achieved an 81% figure of merit (FOM). Our word recognition error rate on this same test set is 54.7%
Keywords :
maximum likelihood estimation; speech recognition; vocabulary; LVCSR system; N-best answers; N-best list; Switchboard Credit Card conversations; Viterbi alignments; conversational speech; figure of merit; hypothesis likelihoods; keyword spotting; large-vocabulary continuous speech recognition; log-likelihood ratio scoring; putative hit; scoring algorithm; test set; word recognition error rate; wordspotting hypotheses; Credit cards; Degradation; Distributed computing; Error analysis; Frequency; Hidden Markov models; High performance computing; Information systems; Management information systems; Resource management; Speech recognition; Testing; Viterbi algorithm;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.479532
