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Articles 1 - 5 of 5
Full-Text Articles in Phonetics and Phonology
Autosegmental Spreading In Optimality Theory, John J. Mccarthy
Autosegmental Spreading In Optimality Theory, John J. Mccarthy
Linguistics Department Faculty Publication Series
Revised December 2009
This paper is a shorter (and probably better) version of "Harmony in Harmonic Serialism." Like its big brother, it argues that Harmonic Serialism answers the conundrum of how iterative autosegmental spreading is obtained in Optimality Theory.
Frank Gouldsmith Speck Collection Index Of Penobscot Materials, Pauleena Macdougall
Frank Gouldsmith Speck Collection Index Of Penobscot Materials, Pauleena Macdougall
Field Notes/Notebooks
No abstract provided.
Prosodylab-Aligner: A Tool For Forced Alignment Of Laboratory Speech, Kyle Gorman, Jonathan Howell, Michael Wagner
Prosodylab-Aligner: A Tool For Forced Alignment Of Laboratory Speech, Kyle Gorman, Jonathan Howell, Michael Wagner
Department of Linguistics Faculty Scholarship and Creative Works
The Penn Forced Aligner automates the alignment process using the Hidden Markov Model Toolkit (HTK). The core of Prosodylab-Aligner is align.py, a script which performs acoustic model training and alignment. This script automates calls to HTK and SoX, an open-source command-line tool which is capable of resampling audio. The included README file provides instructions for installing HTK and SoX on Linux and Mac OS X, and can also be run on Windows. During training, the model is initialized with flat-start monophones, which are then submitted to a single round of model estimation. Then, a tied-state 'small pause' model is inserted …
A Weighted Finite State Transducer Implementation Of Phoneme Rewrite Rules For English-To-Korean Pronunciation Conversion, Hahn Koo
Faculty Publications
Words change their phonetic as well as orthographic form when they are borrowed and used by speakers of another language. A formal model that properly captures this change has theoretical implications in phonology and practical applications in speech processing and machine transliteration. This paper describes a method for developing a finite- state model that predicts how English words and named entities are pronounced in Korean. The model predicts nativized pronunciation using weighted finite-state transducers implementing context-dependent phoneme rewrite rules derived from English-to-Korean pronunciation pairs and syllable phonotactics in Korean.
Lenition, Naomi Gurevich