VowelMeasurement

VowelMeasurement(self, track, heuristic=Heuristic(), vowel_place_dict=lambda: dict()(), reference_values=ReferenceValues(), only_fasttrack=False)

A class used to represent a vowel measurement.

Intended Usage

Certain properties of a VowelMeasurement instance are set by its membership within a VowelClass and that VowelClass’s membership in a VowelClassCollection. These memberships are best managed by passing a list of VowelMeasurements to SpeakerCollection.

vowel_measurements = [VowelMeasurement(t) for t in fasttrack_tracks]
speakers = SpeakerCollection(vowel_measurements)

Parameters

Name	Type	Description	Default
track	fasttrackpy.CandidateTracks	A fasttrackpy.CandidateTrracks object	required
heuristic	Heuristic	A point measurement Heuristic to use. Defaults to Heuristic().	Heuristic()
vowel_place_dict	dict[Literal[‘front’, ‘back’], re.Pattern]	A dictionary of regexes that match front or back vowels.	lambda: dict()()

Attributes

Name	Type	Description
track	fasttrackpy.CandidateTracks	an object of CandidateTracks class
candidates	list	list of candidates for the track
heuristic	Heuristic	an object of Heuristic class (default is Heuristic())
vowel_class	VowelClass	The containing VowelClass object
formant_array	FormantArray	A FormantArray object
file_name	str	name of the file of the track
group	str	TierGroup of the track
id	str	id of the track
interval	aligned_textgrid.SequenceInterval	interval of the track
label	str	label of the track
n_formants	int	number of formants in the track
optimized	int	The number of optimization iterations the vowel measurement has been through.
winner	OneTrack	fasttrackpy.OneTrack The winning formant track
winner_index	int	The index of the winning formant track
cand_param	NDArray[Shape[Param, Formant, Cand], Float]	A array of the candidate DCT parameters.
cand_maxformant	NDArray[Shape[1, Cand], Float]	An array of the candidate maximum formants.
cand_error	NDArray[Shape[Cand], Float]	An array of the candidate smoothing error.
cand_error_logprob_vm	NDArray[Shape[Cand], Float]	Conversion of the smooth error to log probabilities. The candidate with the lowest error = log(1), and the candidate with the largest error = log(0).
cand_param_(mahal/logprob)_speaker_byvclass	NDArray[Shape[Cand], Float]	The mahalanobis distance (mahal) or associated log probability (logprob) for each candidate relative to the VowelClass for this speaker. These are calculated by drawing the relevant mean and covariance matrix from vm.vowel_class
cand_param_(mahal/logprob)_speaker_global	NDArray[Shape[Cand], Float]	The mahalanobis distance (mahal) or associated log probability (logprob) for each candidate relative to all vowel measurements for this speaker. These are calculated by drawing the relevant mean and covariance matrix from vm.vowel_class.vowel_system
cand_param_(mahal/logprob)_corpus_byvclass	NDArray[Shape[Cand], Float]	The mahalanobis distance (mahal) or associated log probability (logprob) for each candidate relative to this vowel class across all speakers. These are calculated by drawing the relevant mean and covariance matrix from vm.vowel_class.vowel_system.corpus
point_measure	pl.DataFrame	A polars dataframe of the point measurement for this vowel.
vm_context	pl.DataFrame	A polars dataframe of contextual information for the vowel measurement.

Methods

Name	Description
to_param_df	Return DataFrame of formant DCT parameters.
to_point_df	Return a DataFrame of point measurements
to_tracks_df	Return a DataFrame of the formant tracks

to_param_df

VowelMeasurement.to_param_df(output='log_param')

Return DataFrame of formant DCT parameters.

Returns

Type	Description
pl.DataFrame	A DataFrame of formant DCT parameters

to_point_df

VowelMeasurement.to_point_df()

Return a DataFrame of point measurements

Returns

Type	Description
pl.DataFrame	A DataFrame of vowel point measures.

to_tracks_df

VowelMeasurement.to_tracks_df()

Return a DataFrame of the formant tracks

Returns

Type	Description
pl.DataFrame	A dataframe with formant track data.