From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition
Abstract
The word error rate (WER), commonly used in ASR assessment, measures the cost of restoring the output word sequence to the original input sequence. However, for most CSR applications apart from dictation machines a more meaningful performance measure would be given by the proportion of information communicated. In this article we introduce two new absolute CSR performance measures: MER (match error rate) and WIL (word information lost). MER is the proportion of I/O word matches which are errors. WIL is a simple approximation to the proportion of word information lost which overcomes the problems associated with the RIL (relative information lost) measure that was proposed half a century ago. Issues relating to ideal performance measurement are discussed and the commonly used Viterbi input/output alignment procedure, with zero weight for hits and equal weight for substitutions, deletions and insertions, is shown to be optimal.
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The final Language Model combination of generic LM (10% weight) and specialized LM (90% weight) has been shown to lead to low Word Error Rate for domain specific applications.
“This weighting has been shown to lead to low Word Error Rate (WER) 3 (Morris et al., 2004) for domain specific applications (Lecouteux et al., 2011).”
From Making Emergency Calls More Accessible to Older Adults Through a Hands-free Speech Interface in the House (2019) · cites this paper as Morris et al., 2004
Additional measures like CER, MER, or WWER have the same underlying problem as WER, as they also quantify the editing distance between two texts.
“Additional measures like the Character Error Rate (CER), the Match Error Rate (MER) [Morris et al. 2004] or the Weighted Word Error Rate (WWER) [Apone et al. 2010] have the same underlying problem, as they also quantify the editing distance between two texts.”
From Measuring the Accuracy of Automatic Speech Recognition Solutions (2023) · cites this paper as Morris et al. 2004
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Versteegh, M., Kuhn, J. D., Synnaeve, G., Ravaux, L., Chemla, E., Cäsar, C., Fuller, J. L., Murphy, D., Schel, A. M., & Dunbar, E. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4954887 · Cited by 5 · see citing works
- Authors
- Morris, A. C., Maier, V., & Green, P.
- ORCID
- Green, Phil
- Venue
- Interspeech 2004
- Publisher
- ISCA
- Published
- 2004-10-04
- Pages
- 2765-2768
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W38194800
- Cited by
- 290
- References
- 6
- Citations by year
- 2026: 39 · 2025: 58 · 2024: 37 · 2023: 44 · 2022: 31 · 2021: 28 · 2020: 8 · 2019: 6 · 2018: 11 · 2017: 2 · 2016: 7 · 2015: 3 · 2014: 4 · 2013: 4 · 2012: 2
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- connected speech recognition, performance indicators, word error rate, Viterbi alignment, information loss
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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