Praat : doing phonetics by computer (version 4.4.24)

By Paul P. G. Boersma. 2006

1,313 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5b204b3605431938d9a8ecf03435cb3894222b65

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Citations in the corpus

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011, methodology): “…of available packages inﬂuenced the development in the recognition of emotion recognition: Firstly tools for feature extraction such as PRAAT 3 (Boersma and Weenink, 2005) or the SNACK sound toolkit, 4 and secondly classiﬁ-cation software and learning environments such as the WEKA 5 (Witten…”
Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. (2009, methodology): “…s. Based upon results of pretests with both adults and children, the F0 of the final word was increased by using the software Praat (Version 4.1.15, Boersma and Weenink 2004) by 35% for the weak incongruity and by 120% for the strong incongruity (without changing the original pitch contour of…”
OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit (2009, unknown): “Most toolkits that include feature extraction algorithms are targeted at speech recognition and speech processing, such as the Hidden Markov Toolkit (HTK) [16], the PRAAT Software [1], the Speech Filling System (SFS) from UCL, and the SNACK package for the Tcl scripting language.”
Opensmile: the munich versatile and fast open-source audio feature extractor (2010, methodology): “Related feature extraction tools used for speech research include e.g. the Hidden Markov Model Toolkit ( HTK ) [15], the PRAAT Software [3], the Speech Filing System ( SFS ), the Auditory Toolbox 4 , a Matlab TM toolbox 5 by Raul Fer-nandez [6], the Tracter framework [7], and the SNACK package for t”
Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease (2008, methodology): “7 Calculation of Traditional Measures Calculation of the traditional measures was performed using the software Praat [34].”
Differential effects of instruction on the development of second language comprehensibility, word stress, rhythm, and intonation: The case of inexperienced Japanese EFL learners (2017, methodology): “A linguistically trained coder analysed word stress and rhythm via auditory impressions, and intonation by listening and visuals (spectrograms) via Praat (Boersma & Weenink, 2012).”
Sounds of male Lake Victoria cichlids vary within and between species and affect female mate preferences (2010, methodology): “We used Praat (Boersma and Weenink 2008) to measure the temporal parameters of the Figure 1 Oscillogram (top) and corresponding sonogram (middle) and frequency spectrum of a sound of pundamilia nyererei, pundamilia pundamilia, and Neochromis omnicaeruleus (from left to right).”
Velar palatalization in Russian and artificial grammar: Constraints on models of morphophonology (2010, methodology): “These weightings were obtained in Praat (Boersma and Weenink 2009) with evaluation noise at 0, plasticity set to 10 with a decrement of 1 and all other settings at default values.”
The Effects of Visual Beats on Prosodic Prominence: Acoustic Analyses, Auditory Perception and Visual Perception. (2007, methodology): “To see if and how the speech signal changed as a function of visual beats, we performed an automatic phonetic analysis of the recorded speech using the Praat software package (Boersma & Weenink, 2006).”
A Supervised Learning Approach to Monaural Segregation of Reverberant Speech (2009, methodology): “we use a priori pitch contours from premixed reverberant targets extracted using Praat [7] in order to remove the influence of pitch errors on the segregation system (see also [28]).”
Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates (2010, methodology): “The minimum, maximum, mean, and range of intensity for each word were extracted using Praat.”
Exploring Monaural Features for Classification-Based Speech Segregation (2013, methodology): “For the pitch-based feature, classifiers are trained on ground truth pitch extracted from clean speech by PRAAT [3], but tested on pitch estimated by a recently proposed multipitch tracker [18].”