interspeech-experiments
Use when designing or auditing the experimental evidence for an INTERSPEECH paper — task-correct metrics (WER/CER, MOS/CMOS, EER/minDCF, PESQ/STOI), baselines reviewers accept, significance testing over utterances and seeds, condition coverage across speakers/noise/languages, and data hygiene for speech corpora.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill interspeech-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# INTERSPEECH Experiments "Not convincing" is the standard Interspeech rejection, and it almost always means the experimental design — not the idea — failed. Speech evaluation has decades of conventions per task; an experiment section that ignores them is illegible to the reviewer pool regardless of how good the numbers are. ## Metric-task law | Task family | Primary metrics | Convention notes | |---|---|---| | ASR | WER / CER | normalization + scorer disclosed; CER for unsegmented scripts | | TTS / VC | MOS, CMOS (+ objective proxies) | panel protocol reported; CMOS for close systems | | Speaker verification | EER, minDCF | official trial lists; DCF prior/costs stated | | Diarization | DER / JER | collar and overlap handling stated | | Enhancement / separation | PESQ, STOI/ESTOI, SI-SDR (+ DNSMOS-style proxies) | wideband vs narrowband named | | SLU / speech translation | intent acc / F1, BLEU/COMET on ASR output | cascaded vs end-to-end made explicit | | Paralinguistics / health | UAR, F1 | speaker-disjoint splits are mandatory | Using a proxy where the community expects the primary (e.g., only neural MOS predictors for a TTS claim) needs an explicit defense sentence. ## Baseline
- Metric-task law
- Baselines that count
- Significance: over what randomness?
- Condition coverage — the speech-specific axis
- Data hygiene
- Designing inside 4 pages
- Worked micro-example: is 4.9 vs 5.6 WER real?
- Negative results and regressions
- Pre-submission experiment audit
- Output format
What does the interspeech-experiments skill do?
Use when designing or auditing the experimental evidence for an INTERSPEECH paper — task-correct metrics (WER/CER, MOS/CMOS, EER/minDCF, PESQ/STOI), baselines reviewers accept, significance testing over utterances and seeds, condition coverage across speakers/noise/languages, and data hygiene for speech corpora.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill interspeech-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.