Video Converter
Audio Converter
Image Converter
PDF Converter
File Compressor
removing nuisance variables from acoustic word embeddings

Removing Nuisance Variables from Acoustic Word Embeddings Obtaining - PowerPoint PPT Presentation

Dec 20, 2022 •541 likes •631 views

Lisa van Staden Removing Nuisance Variables from Acoustic Word Embeddings Obtaining transcriptions is expensive and not always possible. Popular methods for speech processing rely on transcribed speech. 1 Low-Resource Speech and Language

  1. Lisa van Staden Removing Nuisance Variables from Acoustic Word Embeddings

  2. Obtaining transcriptions is expensive and not always possible. Popular methods for speech processing rely on transcribed speech. 1 Low-Resource Speech and Language Processing

  3. • Query-by-Example Search: search speech using speech. We don’t always need to predict text labels: • Tasks need speech segments to be compared. 2 Tasks in LSL Processing

  4. 3 We want to map speech to these representation without using labels. Acoustic Word Embeddings

  5. We want embeddings to be robust. Acoustic properties of speech from different speakers/sexes differ. 4 Nuisance Variables: Speaker and Sex

  6. 5 Current Models

  7. • Improved models: Disentanglement with adverserial training. • Using embeddings in downstream tasks. • Investigate the phonetic information in embeddings. • Links to language acquisition. 6 What’s Next

Recommend

Word Embeddings Natural Language Processing VU (706.230) - Andi Rexha 02/04/2020 Word Embeddings

Word Embeddings Natural Language Processing VU (706.230) - Andi Rexha 02/04/2020 Word Embeddings

Word Embeddings Natural Language Processing VU (706.230) - Andi Rexha 02/04/2020 Word Embeddings Agenda Traditional NLP Word Embeddings-1 Word Embeddings-2 Text preprocessing Topic Modeling ELMo Bag-of-words model Neural

902 views • 64 slides
Word embeddings Rappel Embeddings ( pas Word Embeddings ) Est une lookup table Formalisme:

Word embeddings Rappel Embeddings ( pas Word Embeddings ) Est une lookup table Formalisme:

Word embeddings Rappel Embeddings ( pas Word Embeddings ) Est une lookup table Formalisme: Index dun mot: w i Table dembeddings (lookup matrix): V Embedding: e i e i = V( w i ) Reprsentation dun mot

780 views • 47 slides
Word Embeddings Revisited: Contextual Embeddings CS 6956: Deep Learning for NLP Overview

Word Embeddings Revisited: Contextual Embeddings CS 6956: Deep Learning for NLP Overview

Word Embeddings Revisited: Contextual Embeddings CS 6956: Deep Learning for NLP Overview Word types and tokens Training contextual embeddings Embeddings from Language Models (ELMo) 1 Overview Word types and tokens Training

670 views • 40 slides
Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word embeddings: Early work Word embeddings via language models Word2vec and Glove Evaluating embeddings Design choices and open questions 1

2.01k views • 24 slides
Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word embeddings: Early work Word embeddings via language models Word2vec and Glove Evaluating embeddings Design choices and open questions 1

828 views • 15 slides
Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word embeddings: Early work Word embeddings via language models Word2vec and Glove Evaluating embeddings Design choices and open questions 1

431 views • 17 slides
Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word embeddings: Early work Word embeddings via language models Word2vec and Glove Evaluating embeddings Design choices and open questions 1

1.25k views • 75 slides
Acoustic Acoustic Control Systems BV Acoustic Acoustic Control Systems BV Control Systems BV

Acoustic Acoustic Control Systems BV Acoustic Acoustic Control Systems BV Control Systems BV

Acoustic Acoustic Control Systems BV Acoustic Acoustic Control Systems BV Control Systems BV Control Systems BV GARDEREN GARDEREN THE NETHERLANDS THE NETHERLANDS THE NETHERLANDS THE NETHERLANDS www.acs.eu www.acs.eu 90 80 70 60

616 views • 58 slides
Embeddings @ Twitter Making ML easy with Embeddings !!! Sept 2018 Agenda 1 Team 2 Whats an

Embeddings @ Twitter Making ML easy with Embeddings !!! Sept 2018 Agenda 1 Team 2 Whats an

Embeddings @ Twitter Making ML easy with Embeddings !!! Sept 2018 Agenda 1 Team 2 Whats an Embedding ? 3 Why Embeddings ? 4 Embeddings Pipeline 5 Whats Next Agenda 1 Team 2 Whats an Embedding ? 3 Why Embeddings ? 4

735 views • 40 slides
Word Embeddings Tutorial HILA GONEN PHD STUDENT AT YOAV GOLDBERGS LAB BAR ILAN UNIVERSITY

Word Embeddings Tutorial HILA GONEN PHD STUDENT AT YOAV GOLDBERGS LAB BAR ILAN UNIVERSITY

Word Embeddings Tutorial HILA GONEN PHD STUDENT AT YOAV GOLDBERGS LAB BAR ILAN UNIVERSITY 5/3/18 Outline NLP Intro Word representations and word embeddings Word2vec models Visualizing word embeddings Word2vec in

1.18k views • 63 slides
Mixed membership word embeddings: Corpus-specific embeddings without big data James Foulds

Mixed membership word embeddings: Corpus-specific embeddings without big data James Foulds

Mixed membership word embeddings: Corpus-specific embeddings without big data James Foulds University of California, San Diego Southern California Machine Learning Symposium, Caltech, 11/18/2018 Word Embeddings Language models which learn

540 views • 32 slides
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction Roy Schwartz + ,

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction Roy Schwartz + ,

Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction Roy Schwartz + , Roi Reichart * and Ari Rappoport + + The Hebrew University, * Technion IIT CoNLL 2015 Symmetric Pattern Based Word Embeddings for 2 Improved Word

922 views • 49 slides
Normal Forms for CFGs Eliminating Useless Variables Removing Epsilon Removing Unit

Normal Forms for CFGs Eliminating Useless Variables Removing Epsilon Removing Unit

Normal Forms for CFGs Eliminating Useless Variables Removing Epsilon Removing Unit Productions Chomsky Normal Form 1 Variables That Derive Nothing Consider: S -> AB, A -> aA | a, B -> AB Although A derives all strings of

548 views • 36 slides
Deep convolutional acoustic word embeddings using word-pair side information Herman Kamper 1 ,

Deep convolutional acoustic word embeddings using word-pair side information Herman Kamper 1 ,

Deep convolutional acoustic word embeddings using word-pair side information Herman Kamper 1 , Weiran Wang 2 , Karen Livescu 2 1 CSTR and ILCC, School of Informatics, University of Edinburgh, UK 2 Toyota Technological Institute at Chicago, USA

1.04k views • 76 slides
Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to

Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to

Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to generate vector embeddings? One approach: feedforward neural language models Training a neural language model just to get word embeddings is expensive!

570 views • 38 slides
Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to

Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to

Dense Word Embeddings CMSC 470 Marine Carpuat Slides credit: Jurasky & Martin How to generate vector embeddings? One approach: feedforward neural language models Training a neural language model just to get word embeddings is expensive!

545 views • 35 slides
Review: graphical models Represent a distribution over some RVs using both diagrams and

Review: graphical models Represent a distribution over some RVs using both diagrams and

Review: graphical models Represent a distribution over some RVs using both diagrams and numbers Chief problem: given a GM (the prior ) and some evidence ( data ), compute properties of the conditional distribution P(RVs | data) (the

276 views • 26 slides
Statistics 516 Probability and Statistics for the Physical Sciences Instructor: Peter Bloomfield

Statistics 516 Probability and Statistics for the Physical Sciences Instructor: Peter Bloomfield

ST 516 Experimental Statistics for Engineers II Statistics 516 Probability and Statistics for the Physical Sciences Instructor: Peter Bloomfield Course home page: http://www.stat.ncsu.edu/people/bloomfield/courses/ST516/ 1 / 9 ST 516

353 views • 9 slides
Ev Evolutionary Automation of Coordinated Aut Autono nomous us Vehi hicles Chien-Lun Huang

Ev Evolutionary Automation of Coordinated Aut Autono nomous us Vehi hicles Chien-Lun Huang

Ev Evolutionary Automation of Coordinated Aut Autono nomous us Vehi hicles Chien-Lun Huang (Allen) and Dr Geoff Nitsschke Increased research in adaptive control systems for autonomous vehicles. Existing research focuses on autonomous

1.3k views • 20 slides
The Proof-Search Problem (between bdd-width resolution and bdd-degree semi-algebraic proofs)

The Proof-Search Problem (between bdd-width resolution and bdd-degree semi-algebraic proofs)

The Proof-Search Problem (between bdd-width resolution and bdd-degree semi-algebraic proofs) Albert Atserias Universitat Polit` ecnica de Catalunya Barcelona, Spain Satisfiability Example : 15 variables and 40 clauses x 1 x 2 x 6 x 1

1.17k views • 88 slides
Fitting B/C cosmic-ray data in the AMS-02 era: a cookbook L. Derome, D. Maurin, P. Salati, M.

Fitting B/C cosmic-ray data in the AMS-02 era: a cookbook L. Derome, D. Maurin, P. Salati, M.

Fitting B/C cosmic-ray data in the AMS-02 era: a cookbook L. Derome, D. Maurin, P. Salati, M. Boudaud, Y. Gnolini, and P. Kunz Motivation High quality AMS-02 data (systematics > statistical errors) Need to re-evaluate how analyses

397 views • 10 slides
Fun with HiggsCombine Nick Amin September 6, 2018 Replicating AN numbers Take cards

Fun with HiggsCombine Nick Amin September 6, 2018 Replicating AN numbers Take cards

Fun with HiggsCombine Nick Amin September 6, 2018 Replicating AN numbers Take cards associated with 2016 paper (which were given for the combination) and add lumiscale rateParam * * 1. theorySysts group = pdf alphas isrvar fsrvar scale

603 views • 8 slides
Testimony Combinations: Mathematical Aspects of a Talmudic Problem Ron Adin and Yuval Roichman

Testimony Combinations: Mathematical Aspects of a Talmudic Problem Ron Adin and Yuval Roichman

Testimony Combinations: Mathematical Aspects of a Talmudic Problem Ron Adin and Yuval Roichman Bar-Ilan University radin, yuvalr @math.biu.ac.il Some Basics in Jewish Law Two witnesses are needed to enforce payment of a claimed debt

484 views • 31 slides
Revelation Series Lesson #009 July 18, 2004 Dean Bible Ministries www.deanbibleministries.org

Revelation Series Lesson #009 July 18, 2004 Dean Bible Ministries www.deanbibleministries.org

Revelation Series Lesson #009 July 18, 2004 Dean Bible Ministries www.deanbibleministries.org Dr. Robert L. Dean, Jr. e Se e C u c es o e e at o he Seven Churches of Revelatio Revelation 1:4, John, to the seven churches which are in

554 views • 24 slides

More recommend

Explore More Topics

Stay informed with curated content and fresh updates.

animals pets art culture automotive transportation business finance computer internet construction architecture education-career electronics communication

Logo Sambuz

Unleash a World of Digital Possibilities—Browse, Share, and Explore Content Without Boundaries

Useful Links

About Us Privacy Services FAQ's Contact

Newsletter

Stay Informed & Inspired—Subscribe for the Latest Updates, Tips, and Exclusive Content Directly to Your Inbox.

Mail Us

mail@sambuz.com

2026 SAMBUZ, All right reserved.