ACL RD-TEC 1.0 Summarization of P95-1001
Paper Title:
LEARNING PHONOLOGICAL RULE PROBABILITIES FROM SPEECH CORPORA WITH EXPLORATORY COMPUTATIONAL PHONOLOGY
LEARNING PHONOLOGICAL RULE PROBABILITIES FROM SPEECH CORPORA WITH EXPLORATORY COMPUTATIONAL PHONOLOGY
Authors: Gary Tajchman and Daniel Jurafsky and Eric Fosler
Primarily assigned technology terms:
- algorithm
- automatic system
- automaton
- back-propagation
- back-propagation algorithm
- computational linguistics
- computing
- data collection
- database
- decision tree
- decision trees
- decision-tree
- decoder
- dynamic programming
- dynamic programming search
- estimation algorithm
- estimator
- hidden markov
- hidden markov model
- learning
- likelihood estimation
- markov model
- measuring
- multi-layer perceptron
- parser
- parsing
- perceptron
- phonological parsing
- probability estimation
- recognition
- recognition system
- recognizer
- rule probability estimation
- rule-based approach
- rule-probability estimation
- search
- speech recognition
- speech recognition system
- speech recognizer
- text-to-speech
- text-to-speech system
- viterbi
- viterbi algorithm
- viterbi alignment
Other assigned terms:
- accent
- approach
- comlex
- computational phonology
- conditional probability
- corpora
- derivation
- derivations
- dictionaries
- dictionary
- duration
- estimation
- fact
- feature
- feature vector
- frame
- hypothesis
- knowledge
- labeling
- large corpus
- lexicon
- likelihood
- linguistics
- mapping
- names
- orthography
- parse
- phoneme
- phonemes
- phonological rule
- phonological rule probability
- phonological rules
- probabilities
- probability
- probability estimates
- procedure
- pronunciation
- sentence
- sentences
- speech corpora
- speech corpus
- speech data
- speech input
- speech recognition performance
- spontaneous speech corpora
- stress
- surface form
- tags
- technique
- terms
- training
- training data
- transition probabilities
- tree
- trees
- vowel
- wall street journal corpus
- word
- word string
- words
