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swb_hub_500 WER fullSWBCH Benchmark (Speech Recognition)
Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.
The archive carries no text for this table; the description above is the archive's text for the task Speech Recognition. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: Percentage error (lower is better). Points are placed at the row's paper date; 7 of 12 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | |||||
|---|---|---|---|---|---|---|---|---|
| 1 | IBM (LSTM+Conformer encoder-decoder) | 6.8 | – | Paper | – | 2021 | no code linked | report |
| 2 | IBM (LSTM encoder-decoder) | 7.8 | – | Paper | – | 2020 | no code linked | report |
| 3 | ResNet + BiLSTMs acoustic model | 10.3 | – | Paper | – | 2017 | no code linked | report |
| 4 | VGG/Resnet/LACE/BiLSTM acoustic model trained on SWB+Fisher+CH, N-gram + RNNLM language model trained on Switchboard+Fisher+Gigaword+Broadcast | 11.9 | – | Paper | – | 2016 | no code linked | report |
| 5 | RNN + VGG + LSTM acoustic model trained on SWB+Fisher+CH, N-gram + "model M" + NNLM language model | 12.2 | – | Paper | – | 2016 | no code linked | report |
| 6 | HMM-BLSTM trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher | 13 | – | – | – | not matched | report | |
| 7 | HMM-TDNN trained with MMI + data augmentation (speed) + iVectors + 3 regularizations + Fisher (10% / 15.1% respectively trained on SWBD only) | 13.3 | – | – | – | not matched | report | |
| 8 | CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWB | 16 | – | Paper | Code | 2014 | 9 of 9 ran · 0 unverified | report |
| 9 | HMM-TDNN + iVectors | 17.1 | – | – | – | not matched | report | |
| 10 | HMM-DNN +sMBR | 18.4 | – | – | – | not matched | report | |
| 11 | DNN + Dropout | 19.1 | – | Paper | Code | 2014 | linked, not harvested | report |
| 12 | HMM-TDNN + pNorm + speed up/down speech | 19.3 | – | – | – | not matched | report |
All 12 rows shown. 7 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 1 rows have a graph line, from 1 distinct papers; 1 rows (1 papers) have at least one sample that ran. Counting each paper once: Syntology ran 9 of 9 samples; 0 unverified. Separately, 8 of those 9 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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