ROVER

crowdkit.aggregation.texts.rover.ROVER | Source code

ROVER(
    self,
    tokenizer: Callable[[str], List[str]],
    detokenizer: Callable[[List[str]], str],
    silent: bool = True
)

Recognizer Output Voting Error Reduction (ROVER).

This method uses dynamic programming to align sequences. Next, aligned sequences are used to construct the Word Transition Network (WTN):

Finally, the aggregated sequence is the result of majority voting on each edge of the WTN.

J. G. Fiscus, "A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)," 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, 1997, pp. 347-354. https://doi.org/10.1109/ASRU.1997.659110

Parameters Description

Parameters	Type	Description
`tokenizer`	Callable[[str], List[str]]	A callable that takes a string and returns a list of tokens.
`detokenizer`	Callable[[List[str]], str]	A callable that takes a list of tokens and returns a string.
`silent`	bool	If false, show a progress bar.
`texts_`	Series	Tasks' texts. A pandas.Series indexed by `task` such that `result.loc[task, text]` is the task's text.

Examples:

from crowdkit.aggregation import load_dataset
from crowdkit.aggregation import ROVER
df, gt = load_dataset('crowdspeech-test-clean')
df['text'] = df['text'].apply(lambda s: s.lower())
tokenizer = lambda s: s.split(' ')
detokenizer = lambda tokens: ' '.join(tokens)
result = ROVER(tokenizer, detokenizer).fit_predict(df)

Methods Summary

Method	Description
fit	Fits the model. The aggregated results are saved to the `texts_` attribute.
fit_predict	Fit the model and return the aggregated texts.

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