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Articles 1 - 4 of 4
Full-Text Articles in Computational Linguistics
Towards Explaining Variation In Entrainment, Andreas Weise
Towards Explaining Variation In Entrainment, Andreas Weise
Dissertations, Theses, and Capstone Projects
Entrainment refers to the tendency of human speakers to adapt to their interlocutors to become more similar to them. This affects various dimensions and occurs in many contexts, allowing for rich applications in human-computer interaction. However, it is not exhibited by every speaker in every conversation but varies widely across features, speakers, and contexts, hindering broad application. This variation, whose guiding principles are poorly understood even after decades of entrainment research, is the subject of this thesis. We begin with a comprehensive literature review that serves as the foundation of our own work and provides a reference to guide future …
Analyzing Prosody With Legendre Polynomial Coefficients, Rachel Rakov
Analyzing Prosody With Legendre Polynomial Coefficients, Rachel Rakov
Dissertations, Theses, and Capstone Projects
This investigation demonstrates the effectiveness of Legendre polynomial coefficients representing prosodic contours within the context of two different tasks: nativeness classification and sarcasm detection. By making use of accurate representations of prosodic contours to answer fundamental linguistic questions, we contribute significantly to the body of research focused on analyzing prosody in linguistics as well as modeling prosody for machine learning tasks. Using Legendre polynomial coefficient representations of prosodic contours, we answer prosodic questions about differences in prosody between native English speakers and non-native English speakers whose first language is Mandarin. We also learn more about prosodic qualities of sarcastic speech. …
Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner
Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner
Department of Linguistics Faculty Scholarship and Creative Works
We present a new methodological approach which combines both naturally-occurring speech harvested on the web and speech data elicited in the laboratory. This proof-of-concept study examines the phenomenon of focus sensitivity in English, in which the interpretation of particular grammatical constructions (e.g., the comparative) is sensitive to the location of prosodic prominence. Machine learning algorithms (support vector machines and linear discriminant analysis) and human perception experiments are used to cross-validate the web-harvested and lab-elicited speech. Results con rm the theoretical predictions for location of prominence in comparative clauses and the advantages using both web-harvested and lab-elicited speech. The most robust …
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 …