Jonathan Howell | Montclair State University (original) (raw)
Uploads
Papers by Jonathan Howell
The Journal of the Acoustical Society of America, 2016
The Journal of the Acoustical Society of America, 2014
Journal of the Acoustical Society of America, 2015
The Journal of the Acoustical Society of America, 2009
The Journal of the Acoustical Society of America, 2006
The Journal of the Acoustical Society of America, 2006
Tongue dorsum location and tongue root retraction in alveolar and palatal clicks in the endangere... more Tongue dorsum location and tongue root retraction in alveolar and palatal clicks in the endangered language N/uu. [The Journal of the Acoustical Society of America 120, 3377 (2006)]. Amanda L. Miller, Johanna Brugman, Jonathan Howell, Bonny Sands. Abstract. ...
Proceedings of Acoustics Week in Canada. Canadian Acoustics, 2011
The Penn Forced Aligner automates the alignment process using the Hidden Markov Model Toolkit (HT... more 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 ...
Recent by Jonathan Howell
We present a new methodological approach which combines both naturally-occurring speech harvested... more 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 acoustic classifiers include paradigmatic (i.e., un-normalized), non-intonational acoustic measures (duration and relative formant frequencies from single segments). These acoustic cues are also significant predictors of human listeners’ classification, offering new evidence in the debate whether prominence is mainly encoded by pitch or by other cues, and the role that utterance-normalization plays when looking at non-pitch cues such as duration.
The Journal of the Acoustical Society of America, 2016
The Journal of the Acoustical Society of America, 2014
Journal of the Acoustical Society of America, 2015
The Journal of the Acoustical Society of America, 2009
The Journal of the Acoustical Society of America, 2006
The Journal of the Acoustical Society of America, 2006
Tongue dorsum location and tongue root retraction in alveolar and palatal clicks in the endangere... more Tongue dorsum location and tongue root retraction in alveolar and palatal clicks in the endangered language N/uu. [The Journal of the Acoustical Society of America 120, 3377 (2006)]. Amanda L. Miller, Johanna Brugman, Jonathan Howell, Bonny Sands. Abstract. ...
Proceedings of Acoustics Week in Canada. Canadian Acoustics, 2011
The Penn Forced Aligner automates the alignment process using the Hidden Markov Model Toolkit (HT... more 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 ...
We present a new methodological approach which combines both naturally-occurring speech harvested... more 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 acoustic classifiers include paradigmatic (i.e., un-normalized), non-intonational acoustic measures (duration and relative formant frequencies from single segments). These acoustic cues are also significant predictors of human listeners’ classification, offering new evidence in the debate whether prominence is mainly encoded by pitch or by other cues, and the role that utterance-normalization plays when looking at non-pitch cues such as duration.