ASR overview 2025

 

What Is Audio Speech Recognition?

Audio speech recognition, also called automatic speech recognition (ASR), is a technology that enables computers and devices to understand and process human speech by converting spoken language into text or actionable commands. Fundamentally, ASR captures audio input (via a microphone), digitizes the sound waves, and then processes them through algorithms to recognize phonemes (basic units of sound), assemble them into words, and produce a transcript or trigger specific tasks.twilio+1

Core components and steps include:

• Audio capture and preprocessing: Microphones convert voice vibrations into electrical and then digital signals; preprocessing enhances the speech and reduces noise.

• Acoustic modeling: Maps the digitized signal to phonemes.

• Language modeling: Predicts word sequences using statistical information and context.

• Decoding: Converts the identified phonemes and language models into coherent, context-accurate text.kardome+1

Main Challenges in Speech Recognition

1. Background Noise: Ambient sounds such as traffic, appliances, or other voices can blur the spoken signal, significantly impacting ASR accuracy. While noise suppression exists, it is not perfect, especially in complex real-world environments.milvus+2

2. Accents, Dialects, and Pronunciation Variability: Regional accents, dialects, slang, and non-native pronunciation introduce significant variability, making recognition more difficult if the system isn’t trained on diverse data. Homophones and contextual ambiguities also increase complexity.atltranslate+1

3. Speech Speed and Volume Fluctuations: Variations in how quickly or slowly people speak, as well as changes in loudness, challenge systems optimized for 'average' speech patterns.waywithwords

4. Contextual Understanding: Disambiguating meaning in homophones or similar-sounding words requires context-aware models, which add computational and design complexity.milvus+1

5. Computational Efficiency and Real-Time Processing: Processing long audio streams or interactive tasks with minimal delay demands significant computing resources, balancing accuracy and responsiveness, particularly on mobile or 'edge' devices.milvus

6. Speaker Identification in Multi-Speaker Scenarios: Recognizing who is speaking and tracking speakers accurately is difficult, making transcription and command targeting less reliable in group settings.atltranslate

State of the Art (2025)

a. Neural Network Architectures: Modern ASR models are built on advanced machine learning, particularly neural networks such as transformers, recurrent neural networks, and state-space models. These models excel at mapping speech to text even in challenging acoustic environments.arxiv+1

b. Samba-ASR: The new Samba-ASR model uses a novel state-space architecture, replacing traditional transformers for improved computational efficiency and accuracy. It sets new benchmarks with remarkably low Word Error Rates (WER): as low as 1.17% on LibriSpeech Clean, outperforming previous state-of-the-art models. It is both faster and more adaptable across various languages, domains, and speaking styles.arxiv

c. OpenAI's gpt-4o-transcribe: Recent models like gpt-4o-transcribe improve on earlier whisper-based solutions in accuracy and reliability, especially for diverse accents, noisy environments, and fast or variable speech. These models use reinforcement learning and large-scale, diverse datasets to achieve high performance.openai

d. Multilingual and Accent Robustness: New benchmarks such as ML-SUPERB push models to handle over 150 languages and hundreds of accents, reflecting major progress toward more inclusive, accessible ASR. Models are evaluated on global linguistic diversity and are robust to different speech patterns and background conditions.interspeech2025

In summary, audio speech recognition has evolved into a highly capable, AI-driven field but still wrestles with real-world variability, noise, and linguistic diversity. Today’s best models—like Samba-ASR and GPT-4o—achieve impressively low error rates and operate efficiently, but ongoing research emphasizes even broader language coverage, context awareness, and noise robustnesstness.ibm+2

1. https://www.twilio.com/en-us/blog/insights/ai/what-is-speech-recognition
2. https://opencv.org/blog/applications-of-speech-recognition/
3. https://www.kardome.com/blog-posts/difference-speech-and-voice-recognition
4. https://milvus.io/ai-quick-reference/what-are-common-issues-faced-by-speech-recognition-systems
5. https://waywithwords.net/resource/challenges-in-speech-data-processing/
6. https://www.atltranslate.com/ai/blog/automatic-speech-recognition-challenges
7. https://arxiv.org/html/2501.02832v1
8. https://openai.com/index/introducing-our-next-generation-audio-models/
9. https://www.interspeech2025.org/challenges
10. https://www.ibm.com/think/topics/speech-recognition
11. https://en.wikipedia.org/wiki/Speech_recognition
12. https://developer.nvidia.com/blog/essential-guide-to-automatic-speech-recognition-technology/

courses: deep generative learning and generative AI.

 deep generative learning and generative AI. Here are some notable sources:

• Stanford University: The course "CS236: Deep Generative Models" by Prof. Stefano Ermon provides detailed lecture videos and slides focused on the foundations, challenges, and applications of generative models in image, text, video, medicine, robotics, and more. The course website with slides is available at https://deepgenerativemodels.github.io/ and videos are on YouTube.youtube

• Cornell University: The course "CS 6785: Deep Generative Models," taught by Prof. Vadir Kuleshov, offers an introduction to deep generative models, recent advances, algorithms, and applications including NLP and biology. The lectures are recorded and available on YouTube.youtube

• MIT (Massachusetts Institute of Technology): The "Introduction to Deep Learning 6.S191" program covers deep learning basics along with generative AI applications in media, vision, NLP, and biology. All lecture slides, labs, and code are open-sourced and free to use, accessible at https://introtodeeplearning.com/ with lecture videos like "Generative AI for Media" by Google’s Doug Eck on YouTube.introtodeeplearningyoutube

• Harvard University: There are presentations specifically on generative AI’s role in education, including outlines for PowerPoint slides targeting its teaching and learning impact, available in PDF form (e.g., from Harvard AI Sandbox materials).hcsra.sph.harvard

• University of Virginia (UVA): UVA SEAS offers collections of slides on the technical foundations of generative AI with practical uses in engineering design and analysis.teaching.virginia

• Other resources:

  • NVIDIA Deep Learning Institute has a teaching kit for generative AI with lecture slides, labs, and Jupyter notebooks focused on GPU-accelerated generative AI development.developer.nvidia

  • Stony Brook University provides teaching resources on generative AI including PowerPoint slides for educators.stonybrook

If you want ready-to-use lecture slides or full course materials, the Stanford CS236, Cornell CS6785, and MIT 6.S191 courses are among the most comprehensive and authoritative sources from major universities. Their materials are typically publicly available online for educational use.

Would you like direct access links, or specific slide decks on any of these?

1. https://hcsra.sph.harvard.edu/sites/projects.iq.harvard.edu/files/hcsra/files/presentation_on_ai1.pdf
2. https://www.youtube.com/watch?v=XZ0PMRWXBEU
3. https://blog.uwgb.edu/catl/files/2023/02/Introduction-to-Generative-AI-CATL-Presentation-Slides.pdf
4. https://www.youtube.com/watch?v=IZgvgLy1wyg
5. https://teaching.virginia.edu/collections/uva-seas-resources-teaching-genai-use-for-engineering-design-and-analysis/272
6. https://developer.nvidia.com/blog/nvidia-deep-learning-institute-releases-new-generative-ai-teaching-kit/
7. https://www.sdccd.edu/docs/IIE/ProfessionalDevelopment/Presentations/10252024_AI-Demystified-Intro-to-Generative-AI.pdf
8. https://introtodeeplearning.com
9. https://www.stonybrook.edu/celt/teaching-resources/aibot.php
10. https://www.youtube.com/watch?v=P7Hkh2zOGQ0

medical AI

Artificial Intelligence in Medical Education: The 2025 IACAI Vision and Integration Frameworks

 

https://www.medbiq.org/initiatives/international-advisory-committee-artificial-intelligence

asr whisper wahab

 test run old slurm job and it worked. 

git clone to wahahb

hqin@wahab-01 fairASR25]$ pwd/home/hqin/github/fairASR25

speech-to-text corpora, accent

 speech-to-text corpora—one of which is Meta FAIR’s fairness-oriented dataset:

• LibriSpeech ASR Corpus
  A corpus of roughly 1,000 hours of 16 kHz read English speech, derived from LibriVox audiobooks, carefully segmented and aligned. Released under a CC BY 4.0 license. (openslr.org)

• Multilingual LibriSpeech (MLS)
  A large-scale ASR dataset by Facebook AI Research (Meta), comprising ∼50,000 hours of public-domain audiobooks across eight languages (English, German, Dutch, French, Spanish, Italian, Portuguese, Polish). (Meta AI, voxforge.org)

• Mozilla Common Voice
  A crowdsourced, multilingual speech corpus with millions of volunteer-recorded, validated sentences and transcriptions, released under CC0 (public domain). (Wikipedia)

• TED-LIUM v3
  An English ASR corpus of 452 hours of TED talk recordings with aligned transcripts, freely available for research. (openslr.org)

• VoxForge
  A community-collected GPL-licensed speech corpus in multiple languages, built to support open-source ASR engines (e.g., CMU Sphinx, Julius). (voxforge.org)

• Fair-Speech Dataset (Meta FAIR)
  A fairness-oriented evaluation set containing 26,471 utterances from 593 U.S. speakers, designed to benchmark bias and robustness in speech recognition. (Meta AI, Meta AI)

• GigaSpeech
  A multi-domain English ASR corpus featuring 10,000 hours of high-quality transcribed audio (plus 40,000 hours of additional audio for semi-/unsupervised research).

• VoxPopuli
  Contains over 1 million hours of unlabeled multilingual speech and 1.8 k hours of transcribed speeches in 16 languages (with aligned interpretation pairs), for representation learning and semi-supervised ASR. (arxiv.org)

Here are several publicly available speech-to-text corpora that include regional and non-native accents—many of which you can filter or mine for Southern Chinese (e.g., Cantonese-influenced) accent patterns (such as /s/ vs /ʃ/ or –ing vs –in):

• Speech Accent Archive

  A growing, global collection of ~2,500 English recordings of the same Harvard paragraph, each with narrow phonetic transcription and speaker metadata (including L1 and region). You can browse by “Chinese” and then drill down to Cantonese vs. other dialect regions. (ResearchGate, accent.gmu.edu)

• L2-ARCTIC

  A corpus of non-native English speech from ten Mandarin (plus Hindi, Korean, Spanish, Arabic) speakers reading CMU ARCTIC prompts. It includes orthographic transcripts, forced-aligned phonetic annotations, and expert mispronunciation tags. (psi.engr.tamu.edu)

• CSLU Foreign-Accented English (Release 1.2)

  ~4,925 telephone-quality utterances by speakers of various L1s (including Chinese), with transcript, speaker background, and perceptual accent ratings. (borealisdata.ca)

• speechocean762

  5,000 English utterances from 250 non-native speakers (half children), each annotated at the sentence, word, and phoneme level. Designed for pronunciation assessment, freely downloadable via OpenSLR. (arXiv)

• ShefCE: Cantonese-English Bilingual Corpus

  Audio & transcripts from 31 Hong Kong L2 English learners reading parallel Cantonese and English texts—ideal for studying Cantonese-influenced English phonetics. (orda.shef.ac.uk)

• Sell-Corpus: Multi-Accented Chinese English Speech

  First open-source English speech corpus covering seven major Chinese dialect regions (including Southern dialects), with recordings & transcripts for accent variation research. (sigport.org)

• Mozilla Common Voice

  Crowdsourced, multilingual speech data (CC0) with per-speaker accent tags—you can filter English recordings by “Chinese (Hong Kong)” or “Chinese (Mainland)” to get regional accent samples. (Wikipedia)

• ICNALE Spoken Monologues

  4,400 60-second monologues (~73 h) by 1,100 Asian learners (incl. Mainland China, Hong Kong, Taiwan), with transcripts—useful for comparing Southern vs. Northern Chinese L1 influence on English pronunciation. (language.sakura.ne.jp, language.sakura.ne.jp)

• International Dialects of English Archive (IDEA)

  Free archive of scripted & unscripted English dialect samples worldwide. Browse the “Asia → China” section to find Cantonese- and Mandarin-accented speakers, all with transcripts. (Wikipedia)

Each of these datasets provides aligned audio and text (and often phonetic detail) that you can mine to analyze pronunciation patterns—like the s/ʃ or –ing/–in contrasts—among Southern Chinese speakers learning or using English.

Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems

 Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems

Here are the details:

• Paper: https://arxiv.org/pdf/2001.10494

• Code: https://github.com/kpandey008/carla-aebs

• 
  

 

https://arxiv.org/html/2501.10900v1

A Generative Security Application Engineering Curriculum

https://youtube.com/playlist?list=PLdCTMpWhMq8KuRHaXHuf2U53hM1NkuiCe&si=TqgEulf57ZnUOXON

o All material has been made publicly available (https://codelabs.cs.pdx.edu, https://github.com/wu4f/cs410g-src, https://github.com/wu4f/cyberpdx-crypto)

o YouTube playlist for Generative Security Applications course is available at (https://youtube.com/playlist?list=PLdCTMpWhMq8KuRHaXHuf2U53hM1NkuiCe&si=S5dXzkslHPX5N13w)

o CyberPDX module available at https://crypto.cyberpdx.org

funding acknowledgement fall 2025

 for AI works

HQ thanks USA NSF 2525493 and  2200138, a catalyst award from the USA National Academy of Medicine,  and internal support of the Old Dominion University

ODU CS Candidacy exam

CS  Candidacy Exam.

 

The catalog description of the exam is found at

https://catalog.odu.edu/graduate/sciences/computer-science/computer-science-phd/#additionalrequirementstext

 

He’s doing Option 1, which is summary of papers relevant to his dissertation research topic.

 

The guidelines for the length of the document are just guidelines, it can be longer.  And same for the length of the presentation. It can be as long as the committee would like.

 

goodnight moon audo data and labels?

Children’s Speech Recognition Challenge

https://kidsasr.drivendata.org/

https://github.com/hongqin/goodnight-moon

https://www.drivendata.org/competitions/298/literacy-screening/

shap summry plot by chatGPT

 chatGPT repeatly make mistake for shap summary plot. 

for different class label, the index should be the 3rd position:  shap_vals[:,:,idx],

CPSim: Simulation Toolbox for Security Problems in Cyber-Physical Systems.

 

Liu, Mengyu, et al. "CPSim: Simulation Toolbox for Security Problems in Cyber-Physical Systems." ACM Transactions on Design Automation of Electronic Systems 29.5 (2024): 1-16.

https://github.com/lion-zhang/CPSim/tree/main

A2 consortium 2024 talk links

  Symposium below or share with interested colleagues.

 

1. Full list of Symposium talks
2. Welcome Remarks
3. Pattie Maes, PhD – Opportunities for AI and Wearables to Support Healthy Aging
4. Jianying Hu, PhD – Harnessing AI for Advancing Neurodegenerative Disease Therapeutics
5. Martin Sliwinski, PhD – Managing Cognitive Impairment – From Early Diagnosis to Supportive Care
6. Michael Kahana, PhD – Managing Cognitive Impairment – From Early Diagnosis to Supportive Care
7. Conor Walsh, PhD – Machine Learning and AI Methods for Aging and Dementia Care
8. Marzyeh Ghassemi, PhD -- Machine Learning and AI Methods for Aging and Dementia Care
9. Jordan Smoller, MD – Big Health Data: Challenges and Opportunities
10. Griffin Weber, MD, PhD -- Big Health Data: Challenges and Opportunities

 

 

national family survey of pregancy

  national family survey of pregancy

https://www.cdc.gov/nchs/nsfg/index.htm

todo: request to restrickted access variables. 

Fall 2025 schedule

August 23 - Dec 12, 2025, Thursday 6p - 8:40pm.  

Scheduled Meeting Times

Type	Time	Days	Where	Date Range	Schedule Type	Instructors
Scheduled In-Class Meetings	6:00 pm - 8:40 pm	R	ENGINEERING & COMP SCI BLDG 2120	Aug 23, 2025 - Dec 12, 2025	LECTURE

ZIP Code RUCA Approximation,

 

https://depts.washington.edu/uwruca/ruca-approx.php?utm_source=chatgpt.com

All of Us survey, data codebooks

 All of Us survey, data codebooks

https://docs.google.com/spreadsheets/d/1pODkE2bFN-kmVtYp89rtrJg7oXck4Fsex58237x47mA/edit?usp=sharing

A model for the assembly map of bordism-invariant functors

 The paper "A model for the assembly map of bordism-invariant functors" by Levin, Nocera, and Saunier (2025) develops advanced categorical frameworks for algebraic topology, particularly through oplax colimits of stable/hermitian/Poincaré categories and bordism-invariant functors123. While not directly addressing machine learning (ML) or large language models (LLMs), its contributions could indirectly influence these fields through three key pathways:

1. Enhanced Categorical Frameworks for ML

The paper's formalization of oplax colimits and Poincaré-Verdier localizing invariants13 provides new mathematical tools for structuring compositional systems. This could advance:

• Model Architecture Design: By abstracting relationships between components (e.g., neural network layers) as bordism-invariant functors, enabling more rigorous analysis of model behavior under transformations5.

• Geometric Deep Learning: Topological invariants and assembly maps could refine methods for learning on non-Euclidean data (e.g., graphs, manifolds) by encoding persistence of features under deformations5.

2. Invariance Learning and Equivalence

The bordism-invariance concept—where structures remain unchanged under continuous deformations—offers a mathematical foundation for invariance principles in ML:

• Data Augmentation: Formalizing "bordism equivalence" could guide the design of augmentation strategies that preserve semantic content (e.g., image rotations as "topological bordisms")5.

• Robust Feature Extraction: Kernels of Verdier projections13 might model noise subspaces to exclude during feature learning, improving adversarial robustness.

3. LLMs for Structured Reasoning

The paper’s explicit decomposition of complex functors (e.g., Shaneson splittings with twists13) parallels challenges in LLM-based reasoning:

• Program Invariant Prediction: LLMs that infer program invariants6 could adopt categorical decompositions to handle twisted or hierarchical constraints (e.g., loop invariants in code).

• Categorical Data Embeddings: LLM-generated numerical representations of categorical data4 might leverage bordism-invariance to ensure embeddings respect equivalence classes (e.g., "color" as a deformation-invariant attribute).

Limitations and Future Directions

The work is highly theoretical, with no direct ML/LLM applications in the paper. Bridging this gap requires:

• Translating topological bordisms into data-augmentation pipelines.

• Implementing Poincaré-Verdier invariants as regularization terms in loss functions.

• Extending LLM-based invariant predictors6 to handle categorical assembly maps.

While speculative, these connections highlight how advanced category theory could enrich ML’s theoretical foundations and LLMs’ reasoning capabilities.

1. https://arxiv.org/abs/2506.05238
2. https://arxiv.org/pdf/2506.05238.pdf
3. https://www.arxiv.org/pdf/2506.05238.pdf
4. https://pubmed.ncbi.nlm.nih.gov/39348252/
5. https://www.aimodels.fyi/papers/arxiv/category-theoretical-topos-theoretical-frameworks-machine-learning
6. https://openreview.net/pdf?id=mXv2aVqUGG
7. https://x.com/CTpreprintBot
8. https://keik.org/profile/mathat-bot.bsky.social
9. https://www.alphaxiv.org/abs/2506.05238
10. https://publications.mfo.de/bitstream/handle/mfo/4263/OWR_2024_47.pdf?sequence=1&isAllowed=y
11. https://x.com/CTpreprintBot/status/1930943445977518380
12. https://www.themoonlight.io/en/review/a-model-for-the-assembly-map-of-bordism-invariant-functors
13. https://library.slmath.org/books/Book69/files/wholebook.pdf
14. https://www.reed.edu/math-stats/thesis.html
15. https://math.mit.edu/events/talbot/2020/syllabus2020.pdf
16. https://webhomes.maths.ed.ac.uk/~v1ranick/papers/quinnass.pdf
17. https://msp.org/agt/2009/9-4/agt-v9-n4-p16-s.pdf
18. https://webhomes.maths.ed.ac.uk/~v1ranick/papers/owsem.pdf

Mendely preprint error

 In Mendely, if an article has unspecified type, it often list it as "preprint". 

To fix this, just change the document type to 'jounral article' or 'conference proceedings' or other appropriate type. 

Beyond Attention: Toward Machines with Intrinsic Higher Mental States

 

Beyond Attention: Toward Machines with Intrinsic Higher Mental States

https://techxplore.com/news/2025-05-architecture-emulates-higher-human-mental.html#google_vignette

USDA Rural-Urban Commuting Area Codes

 USDA

Rural-Urban Commuting Area Codes

https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes?utm_source=chatgpt.com

three digit 360 zipcode in Alabama

 Based on our discussion, I have looked further into the ‘360’ zipcode region, and found it contain 14 counties below:

 

counties = [    "Autauga County", "Barbour County", "Bullock County", "Butler County",

    "Chilton County", "Coosa County", "Covington County", "Crenshaw County",

    "Elmore County", "Lowndes County", "Macon County", "Montgomery County",

    "Pike County", "Tallapoosa County"]

 

Amon them, there are “Barbour”, “Bullock”, and “Macon” counties.

 

So, not sure how useful the three-digit zip code of ‘360’ might be relevant to TU MCH project. 

pastbin

 https://pastebin.com/uBcFUXCA

1. import pandas as pd
2. dataset = %env WORKSPACE_CDR
3. query = """
4. SELECT
5. p.person_id AS person_id,
6. c.concept_name AS state_name
7. FROM `{dataset}.person` AS p
8. LEFT JOIN `{dataset}.concept` AS c ON p.state_of_residence_concept_id = c.concept_id
9. WHERE c.concept_name LIKE 'PII State: %'
10. """
11.  
12. state_df = pd.read_gbq(query.format(dataset = dataset))
13.  
14. state_df['state_of_residence'] = state_df['state_name'].str.replace('PII State: ', '')
15.  
16. state_df.head()
17. state_df.shape
18.  
19. homeless_state_df=state_df[state_df['person_id'].isin(homeless_respiratory_status['person_id'])]
20. homeless_state_df['state_of_residence'].value_counts()