![]() The first two characters in the filenames reflect the dialect region of the talker (PN = Pacific Northwest, NC = Northern Cities). Sentence identification numbers are derived from the “list-sentence” notation of the original IEEE sentence lists: for example, sentence 01-07 corresponds to sentence #7 from list #1 of the original numbering scheme. Individual transcript files for each sentence are also included. Transcripts of the 180 sentences (along with their identification numbers) are included in the corpus in tab-delimited format. The sentence texts are drawn from the IEEE “Harvard” set. They have NOT been checked or corrected by humans (much less by well-trained phoneticians or speech scientists). These are TextGrids for use with the praat software that have been automatically generated by the Penn Phonetics lab forced aligner software and are known to contain misalignments. The set of audio files has been RMS-normalized to equate intensity across all recordings in the corpus.Ī set of 3600 time-aligned transcriptions are included in the corpus. Files are readings of 180 sentences by 20 different talkers (5 males and 5 females from each of two dialect regions of American English: the Pacific Northwest and the Northern Cities). The corpus includes 3600 audio files in WAV format, sampled at 44.1 kHz with 16-bit depth. A., Haywood, J., Gehani, N., & Rudolph, S. You can download the entire corpus (in compressed. All information found here is also contained in the README file included with the corpus. This page contains information about the UW/NC corpus. 55–83, 2011.The University of Washington/Northwestern University (UW/NU) Corpus 1.0 Hirst, "The Analysis by Synthesis of Speech Melody: from Data to Models," Journal of Speech Sciences, vol. How to citeĬlick on the DOI badge above to see instructions on how to cite the script. See the LICENSE file for license rights and limitations. See the CHANGELOG file for the complete version history. If the GUI option 'Inspect' option is selected, the user can manually unvoice frames that s/he considers to be errors after the second pass. The 'Range' option provided in the GUI menu lets the user select between the two constant values (1.5 or 2.5). Hirst also suggests that 2.5 * q 3 can give a better estimation of ceiling for expressive speech. Actually, Hirst suggests 0.75 as a coefficient for q 1, but in my empirical experience 0.75 seems to result in a floor value that is slightly too high and thus exclude some bona fide f0 candidates. This heuristic is suggested by Hirst (see Reference). Where q 1 and q 3 are respectively the first and third quartiles of the f0 values contained in the first Pitch object. The optimized values are obtained using the following formulae: In the second pass, another Pitch object is extracted using optimal values for floor and ceiling, estimated from the first Pitch object. In the first pass the Pitch object is extracted using 50 and 700 Hz as floor and ceiling estimates. The relevant parameters the algorithm manipulates are floor and ceiling f0 values. The f0 extraction is a two-pass operation. When the user is done the script's execution will continue. The script will first extract the f0 contour in a two-pass operation and then prompt the user to inspect the Pitch object and remove or add pitch points as s/he sees fit. The user can select a Sound object from the Objects list or choose a sound file in a folder and the script will generate a Pitch object based on to the algorithm described below. The script optimizes the range parameter (floor and ceiling values) passed to Praat's F0 autocorrelation-based extraction algorithm. ![]()
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