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Introduction: Knowledge in speech and language processing,
ambiguity, Models and algorithms, language, though and understanding.
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Regular Expression and Automata: Regular expressions
Basic regular expression patterns, Disjunction, Grouping and Precedence,
Finite-State Automata-Using and FSA to recognize shoptalk, Using NFSA to
accept strings.
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Morphology and Finite State Transducers: Survey of English
Morphology Inflectional and derivational Morphology, Finite-state
morphology parsing-Lexicen and Morphnotacties, Morphological parsing with
Finite-State Transducers, Orthographic Rules and Finite-State Transducers.
Combining FST lexicons and rules. Lexicon free FSTs and Human
Morphological Processing.
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Computational Phonology and Text To Speech: Speech
sounds and phonetic Transcription- Consonants; place and Manner of
articulation and vowels. Phonemic, Phonology Rules and Transducers.
Advanced Issues in computational phonology- Harmony, Templates Morphology
and Optimality theory. Machine Learning of phonological Rules, Mapping
Text to Phones for TTS, Prosody in TTS and Human, Processing of Phonology
and Morphology.
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HMMs and Speech Recognition: Speed Recognition Architecture,
Hidden Markov Models, Viterbi Algorithm and A* Decoding, Acoustic
processing of Speed-Sound Waves, Waveform interpretation and spectra.
Training a Speech Recognizer and Human Speech Recognition.
Recommended
Books:
1
Daniel Jurafsky Speech and Language Processing, Pearson
Education, Asia. |