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Phonetically-Oriented Word Error Rate aligner (POWER)

Levenshtein-based word alignment and word error rate for ASR that accounts for acoustic confusability.

POWER is a variation to the commonly used Word Error Rate (WER) metric for speech recognition evaluation which incorporates the alignment of phonemes, in the absence of time boundary information. After computing the Levenshtein alignment on words in the reference and hypothesis transcripts, spans of adjacent errors are converted into phonemes with word and syllable boundaries and a phonetic Levenshtein alignment is performed. The phoneme alignment information is used to correct the word alignment labels in each error region. POWER yields similar scores to WER with the added advantages of better word alignments and the ability to capture one-to-many alignments corresponding to homophonic errors in speech recognition hypotheses. These improved alignments allow a better tracing of the impact of Levenshtein error types in speech recognition on downstream tasks such as speech translation.

Status

This repository currently contains a simplified version of POWER that doesn't rely directly on TTS. I'm continuing to move parts of the codebase from a private repository here for the community to benefit. It's an iterative roll-out because of some dependency breaks since I built this back in 2015. Thanks for your patience!

Current check-ins

Currently the repo has:

  1. A simple Levenshtein aligner checked in. See lev.py for details.
  2. The power aligner. See power.py for details.
  3. Alternative implementation of phoneme alignments with CMUDict (http://www.speech.cs.cmu.edu/cgi-bin/cmudict) and Hunspell for syllabification (e.g. https://pyphen.org/) instead of Festival

Examples

lev.py - Standard Levenshtein alignments

python lev.py examples/align-words/ref.txt examples/align-words/hyp.txt

power.py - Phonetically-oriented alignments

python power.py --ref examples/align-words/ref.txt --hyp examples/align-words/hyp.txt --output examples/align-words/results --lexicon lex/cmudict.rep.json --show-confusions txt

Example POWER alignment

Scores (#C #S #D #I) 7 8 0 1
REF:  you     know  cadaver  dissection       is    the  traditional  way  of  learning  human  anatomy       
HYP:  seeing  a     cadaver  dissection  and  ease  the  traditional  way  of  loaning   human  and that to me
Eval: S       S     C        C           I    S     C    C            C    C   S         C      S             

Correct               =  58.3%   7   (12)
Substitutions         =  66.7%   8   (12)
Deletions             =  0.0%   0   (12)
Insertions            =  8.3%   1   (12)

Errors                =  75.0%   9   (12)

Ref. words            =         12   (12)
Hyp. words            =         16   (12)
Aligned words         =         15   (12)

Use the --show-confusions command to view the substitution errors in your evaluation set.

There are currently a few imperfections due to not using a proper grapheme to phoneme (g2p) transducer like in Festival. If you see any significant alignment issues, please open up an Issue.

Next steps

Punctuation and alignment

  • Character alignment option instead of phoneme alignment back-off
  • Reintegrate oracle punctuation insertion (i.e. Reference contains punctuation, insert the same punctuation symbols on the hypothesis)
  • Reintroduce punctuation splitting and tokenization

Papers

When referencing power-asr, please cite this paper.

@inproceedings{power-asr, 
    author={Nicholas Ruiz and Marcello Federico}, 
    booktitle={{2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)}}, 
    title={Phonetically-oriented word error alignment for speech recognition error analysis in speech translation}, 
    year={2015}, 
    volume={}, 
    number={}, 
    pages={296-302}, 
    keywords={error analysis;speech recognition;word processing;phonetically-oriented word error alignment;speech recognition error analysis;speech translation;word error rate metric;WER metric;speech recognition evaluation;time boundary information;syllable boundaries;phonetic Levenshtein alignment;phoneme alignment information;word alignment labels;phonetically-oriented word error rate;POWER;speech recognition hypothesis;Speech recognition;Error analysis;Speech;Measurement;Matrices;Pragmatics;Analytical models;automatic speech recognition;speech translation;mixed-effects models;error analysis}, 
    doi={10.1109/ASRU.2015.7404808}, 
    ISSN={}, 
    month={Dec},
}

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