Paper Info
Title
Two-pass strategy for continuous speech recognition with detection and transcription of unknown words.
Abstract
This paper proposes a new approach of using a two-pass strategy to effectively recognize continuous speech including unknown words. In this approach, the first pass uses context-independent phoneme HMMs to recognize registered words and phoneme-cluster HMMs to detect unknown words. In the second pass context-dependent phone models are used for precise recognition where unknown words are transcribed. In sentence recognition experiments using this unknown-word processing, phoneme cluster models that consider the Japanese syllabic construction achieved a higher word accuracy rate of 70.3%, compared with 59.2% for sentence recognition without this processing. Furthermore, the amount of processing was reduced by about half compared with a detection method using phoneme HMMs. The total system achieves a 75.2% phoneme accuracy rate including the transcription of unknown words.
Year
DOI
Venue
1996
10.1109/ICASSP.1996.541152
ICASSP
Keywords
Field
DocType
phoneme accuracy rate,phoneme-cluster hmms,precise recognition,phoneme cluster model,unknown word,sentence recognition experiment,context-independent phoneme hmms,two-pass strategy,continuous speech recognition,phoneme hmms,sentence recognition,unknown-word processing,speech processing,context dependent,context modeling,hidden markov models,natural languages,speech recognition
Speech processing,Syllabic verse,Pattern recognition,Computer science,Speech recognition,Context model,Natural language,Phone,Artificial intelligence,Hidden Markov model,Text processing,Word accuracy
Conference
ISBN
Citations 
PageRank 
0-7803-3192-3
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
S. Matsunaga100.34
Hiroyuki Sakamoto2345.24