You can create your own file named dict.local that contains pronunciations of any missing words. If a word is missing, then the aligner does not have the pronunciation information it requires to complete alignment. Your sound file should have a sampling rate that is equal to or greater than the acoustic model sampling rate.Įvery word in your transcript must exactly match a word in the master dictionary, which is in the file /opt/p2fa/model/dict in the BPM (from the CMU Pronouncing Dictionary). The -r option determines which set of acoustic models to use (I would recommend that you use 16000). e end_time - end of portion of wavfile to align (in seconds, defaul to end) s start_time - start of portion of wavfile to align (in seconds, default 0) r sampling_rate - override which sample rate model to use, one of 8000, 11025, and 16000 > pyalign wave_file transcript_file output_file (The Penn tool is named align.py, and pyalign is a simple wrapper that makes align.py easier to call in the context of the BPM.) Use the pyalign command to do forced alignment. Praat scripting can then be used to extract phonetic measurements, or you can read the textgrid in a python script ( meas_formants for an example) and use the ESPS unix command-line acoustic analysis package to extract phonetic measurements. This is a text file that can be read into Praat as a textgrid. Your transcription must include every single utterance, including false starts, filled pauses such as “um,” “uh,” or any other sort of hesitation. wav file, and needs to know the order in which they are spoken (and may also need to know about disfluencies, laughter, etc. ![]() The aligner needs to know what words are spoken in the. Just be sure that the sampling rate of your wav file is at least as fast as the acoustic models you specify. ![]() One thing to keep in mind is that if you specify that you want the 16kHz acoustic models to be used, but you pass an 11.025 kHz file to the aligner the performance will be degraded. The aligner uses sox to create a copy of your wav file that has all of the properties that are needed for HTK. The pyalign command has three required arguments: You should familiarize yourself with pyalign even if you intend to use multi_align since multi_align is just a convenient way to iteratively call pyalign for the individual labels in a TextGrid. The multi_align command is used for more complicated situations involving multiple utterances, multiple speakers, or multiple input channels. įor simple alignments involving a single utterance you can call pyalign directly. Regardless, you will need to register to use the HTK toolkit, at. You may be able to set this up on your home computer, but most people will find it easier to run it through the BPM. It is implemented on PhonLab BPM using sox and the HTK library of automatic speech recognition software. We used this system in the "voices of Berkeley" project to find vowel midpoints and take formant measurements automatically. It produces a Praat textgrid file that has word and phone boundaries for the speech in a wav file that you give to the aligner. The aligner is an implementation of the Penn forced aligner (Jiahong Yuan), which is based on the HTK speech recognition toolkit. While automatic alignment does not yet rival manual alignment, the amount of time gained through forced alignment is often worth the small decrease in accuracy for many projects.įorced alignment works best on recordings whichīut other types of recordings may also be well processed. 2.4 Sharing a dict.local with a Google Drive spreadsheetįorced alignment refers to the process by which orthographic transcriptions are aligned to audio recordings to automatically generate phone level segmentation. ![]()
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