C88-1082 also carried out using bottom-up phoneme recognition results \ -LSB- 12 \ -RSB- .
H89-2047 results for speaker-independent phoneme recognition . We performed experiments on
N09-1021 due to the considerably improved phoneme recognition afforded by longer recognition
C88-1082 parameters are extracted and bottom-up phoneme recognition is carried out . The phrase hypotheses
C88-1082 feature extraction part and a phoneme recognition part \ -LSB- 10,11 \ -RSB- .
P08-2042 manual transcription . Over 85 % phoneme recognition accuracy is demonstrated for
C88-1082 from errors and rejections in phoneme recognition and greatly increases the accuracy
P05-1064 P-PRLM . Figure 1 . L monolingual phoneme recognition front-ends are used in parallel
C88-1082 . In such methods , however , phoneme recognition errors and rejections result
H94-1061 would appear that although higher phoneme recognition rates are achieved for French
P08-5004 parsing , entity extraction , and phoneme recognition . Our algorithmic framework will
C88-1082 and phrase recognition based on phoneme recognition , the parser extracts the sentence
H89-2033 was used very effectively in a phoneme recognition system ( McDermott , 1989 ) .
N09-1021 from the low accuracy typical of phoneme recognition . We consider two methods for
H89-2047 that this result also holds for phoneme recognition , when phoneme segmentation boundaries
N09-1021 using standard word-based LVCSR , phoneme recognition , and LVCSR using phoneme multigrams
J88-2015 +65 dB , measured 64 % correct phoneme recognition . It was estimated that an effective
H92-1100 the dynamical system model in phoneme recognition ( as opposed to classification
H89-2047 the Stochastic Segment Model for Phoneme Recognition </title> V Digalakis t M Ostendofft
C88-1082 phrase hypotheses for top-down phoneme recognition are pre-selected by the { 1 }
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