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--- title: "talkbank" source: https://www.jemoka.com/posts/kbhtalkbank/ --- Moved much of this to Drafts instead phonbank: poor articulation disfluent kids late talkers Write a review about ASR benchmark methods REV would be our benchmark What corpora we use? Has anyone used disordered speech? Or really seriously accented speech vis a vi CORALL (how was CORALL sampled?) What samples? How do we sample? What are the benchmarks? ASR model + WER tildes and noprompt swapped WER missing words correct alignment things swap noprompt backwards apostrophies for quotes the word separation error put tilde BETWEEN specific symbols with connection symbols jemoka becomes batchalign 2 Extended UD? combining bash script to run batchalign multiple times throughout the directories Removing removing non-auditory SBCA corpus area Diarization Diarization as a Bi-product of ASR humans at the end do speaker ID in the end DO TO BATCHALIGN allow people to reject files runhouse meeting <>Donny Greenberg: ADNE, nurses’ health implementation at Google: grantees of canniniminty Remaining questions but we can’t provide SSH function.save() remote running through hashicorp vault? serializing ssh key remote? RUNHOUSE call into remote! headscale take wave2vec and hubert and GSLM questions? ask about inter-turn pauses, where INV: something something something <- PAR: WWW <- INV: somethingsomething else <- PAR: words words word no bullets are given for PAR, so do we skip it? do we count the time for WWW all as an inter-turn pause between INV and PAR? etc. Per Turn Turn level analysis Rename tier to Silence duration? does it include inter-utterance pauses? within-utterance pause fluency, mechanistic between-utterance pause pause between utterances also: between-speaker pause! leaves room for the speaker to take the floor BETWEEN speaker pauses: “I don’t know what you are asking me”, etc.: “breakdown!” add features: STOPPA, TRESTLE, Wang https://coryshain.github.io/ featurize saturnino fausa Questions What features? Where to put them? TalkBankDB How to encode the features? “How informative are your features” Start coming up with features (TRESTLE, perhaps) Encode them into xarray <> saturnino stuff make Spanish names list name, city, countries corpuses SABSAE: santa barbara english CABNC: British english next ignore any words that goes wrong in the pipeline ~change: noun => n; verb => v, etc.~ DET: ignore “DEF”, or perhaps the entir featureless unbulleted VAD exprimentents errors! line 1492 *PAR: so ‡ anyway I tiptoe to the front door , open the front door and walk in . •1045194_1050644• %mor: co|so beg|beg adv|anyway pro:sub|I v|+n|tip+n|toe prep|to det:art|the n|front n|door cm|cm adj|open det:art|the n|front n|door coord|and n|walk adv|in . %gra: 1|0|BEG 2|1|BEGP 3|5|JCT 4|5|SUBJ 5|0|ROOT 6|5|JCT 7|9|DET 8|9|MOD 9|6|POBJ 10|5|LP 11|14|MOD 12|14|DET 13|14|MOD 14|5|OBJ 15|14|CONJ 16|15|COORD 17|16|NJCT 18|5|PUNCT errors? words without features needs to be correctly handled (done in the middle of meeting) 04111 (me ma SOS) nouns shouldn’t mark if it is Com,Neut, should’nt mark if its Com fix PASTP => PAST and does past participles exist? more Move shua to d(e) Include instructions on how to recreate a broken Conda environment Update the package to conda somehow move next steps deal with `n` +… fix remove bullets results ~ contraction & fused suffix getting rid of punkt in mor , => cm . => no PUNKT, stays stuff chocolaty (noadmin, https://docs.chocolatey.org/en-us/choco/setup#non-administrative-install) miniconda setx path “%path%;C:\tools\miniconda3\condabin” curl env first, the install (Windows can’t do it from a URL) readme conda init zsh (close shell, open again) .mp4 mfa model downloading what’s the difference between online docker install and manual install NLTK Huggingface transformers tokenizers (versining) /opt/homebrew/Caskroom/miniforge/base/envs/aligner/lib/python3.9/site-packages/montreal_forced_aligner/corpus/text_corpus.py; getattr(self, k).update(error_dict[k]) AttributeError: ’list’ object has no attribute ‘update’ FileArgumentNotFoundError: ; line 139 DBA See the data on the frequency of haphax legomina vs. COCA ESPNet need to talk to Ji Yang Andrew’s Features Collapse two PAR tiers down Checkpoint per file One corpus prompt per run Handle empty tiers I/P selection crashes! contingency preview the LONGEST segment instead of the top one -i kill in the middle fixes “my mom’s cryin(g)” [<] mm [l648] (also themmm after) “made her a nice dress” [<] mhm [l1086] “when I was a kid I” &=laughs [l1278] Others chstring (for uh, mm-hmm) retrace (asr&fa folder) lowcase (caps) rep-join.cut (fixes/) numbers <affirmative> ‘mo data! CallFriend/CallHome (ca-data) ISL? SBCSAE Aphasia + MICASE TBI data Providing a Two-Pass Solution Writing Big description of the pipeline Notion of the pipeline Better tokenization? 8/18 Initial segment repetition Extracting studdering Gramatically problematic mar mar has done a thing and its phoneme level We did it, now automated LEAP data next actions Aphasia (-apraxia?): classification Child data (EllisWeismer) Dementia a ~Multiple @Begin/CHECK problem~ ~Placement of @Options~ ~Strange, missing period~ ~Bracket comments should FOLLOW words instead of PRECEEDING them~ ~%xwor: line~ STICK TO DASHES WHEN DISTRIBUTING BATCHALIGN end the utterance when it ends (incl. inter-utterance pauses) “I” need to be capitalized 11005 (LT) Align EllisWeismer Also cool to align: fluency IISRP/* https://en.wikipedia.org/wiki/Speaker_diarisation https://universaldependencies.org/ Alzheimer’s Project https://dementia.talkbank.org/ https://luzs.gitlab.io/adresso-2021/ Specifically: https://dementia.talkbank.org/access/English/Pitt.html Review Kathleen Fraser: https://drive.google.com/drive/u/1/folders/1lYTIzzXLXw3LlDG9ZQ7k4RayDiP6eLs1 Here are the review papers: https://drive.google.com/drive/u/1/folders/1pokU75aKt6vNdeSMpc-HfN9fkLvRyutt Read this first: https://drive.google.com/drive/u/1/folders/0B3XZtiQwQW4XMnlFN0ZGUndUamM?resourcekey=0-AlOCZb4q9TyG4KpaMQpeoA Some PITT data have 3-4 recordings The best way to diagnosing alzhimers’ is from language. Why this field is needed: to analyze a pre-post test metric. Desired output: existence of dementia (a.k.a alzheimer’s’). Other research to read: Penn (julia parish something but they don’t stare their data but they smile and things with Mark Libermann type of thing) Learning more about speech text https://my.clevelandclinic.org/health/diagnostics/22327-differential-diagnosis python3 ~/mfa_data/batchalign-dist/batchalign.py ~/mfa_data/my_corpus ~/mfa_data/my_corpus_aligned christan marr paper on MFA on child data