M.Sc. Language Science and Technology Bridge Course, Oct. 2011. Phonetics Oct 18-19, 2011 Bernd Möbius FR 4.7, Phonetics Saarland University. Levels of linguistic description. Phonetics Phonology Morphology Lexicon Syntax Semantics Pragmatics PsycholinguisticsBy niveditha
Conditional Random Fields for Automatic Speech Recognition. Jeremy Morris 05/12/2010. Motivation. What is the purpose of Automatic Speech Recognition? Take an acoustic speech signal … … and extract higher level information (e.g. words) from it. “speech”. Motivation.By tausiq
EM. Advanced Statistical Methods in NLP Ling 572 March 6, 2012. Slides based on F. Xia11. Roadmap. Motivation: Unsupervised learning Maximum Likelihood Estimation EM: Basic concepts Main ideas Example: Forward-backward algorithm. Motivation. Task: Train a speech recognizerBy shay
Acoustic Databases. Jan Odijk ELSNET Summer School, Prague, 2001. Acknowledgements. Part of the slides have been borrowed from or are based on work by Bart D’Hoore Hugo van Hamme Robrecht Comeyne Dirk van Compernolle Bert van Coile. Overview. What is a speech database?By kesia
Automatic Speech Recognition Introduction. Jan Odijk Utrecht, Dec 9, 2010. Overview. What is ASR? Why is it difficult? How does it work? How to make a speech recognizer? Example Applications. Overview. What is ASR? Why is it difficult? How does it work?By isonm
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Speech Signal Representations. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References: 1. X. Huang et. al., Spoken Language Processing , Chapters 5, 6
Acoustic Modeling for Speech Recognition. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References: 1. X. Huang et. al. Spoken Language Processing . Chapters 8, 4 2. S. Young. The HTK Book (HTK Version 3.4). Introduction.
1999 MATLAB Conference, Singapore. Speech/Audio Signal Processing. J.-S. Roger Jang ( 張智星 ) CS Dept, Tsing-Hua Univ, Taiwan ( 清華大學 資訊系 ) http://www.cs.nthu.edu.tw/~jang email@example.com. Outline. Wave file manipulation Reading, writing, recording ... Time-domain processing
Analyzing the Speech Signal. Julia Hirschberg CS 6998. Basic Acoustics. What is sound? Pressure fluctuations in the air caused by a musical instrument, a car horn, a voice Cause eardrum to move Auditory system translates into neural impulses Brain interprets as sound How does it travel?
Speech Signal Processing I. Edmilson Morais and Prof. Greg. Dogil October, 25, 2001. Second Class. The Speech Signal Digitalization Digital filters : FIR , IIR Linear Systems Fourier Analysis Z-Transform Z-Transform and Linear Systems Sampling Theorem
Speech Signal Processing I. By Edmilson Morais And Prof. Greg. Dogil Second Lecture Stuttgart, October 25, 2001. The Speech Signal. No-stacionary signal Voiced – almost periodic (Concept of pitch ) Unvoiced (aleatory) Transitions (Bursts, ...) Range of the Pitch Male :
Signal Subspace Speech Enhancement. Presentation Outline. Introduction Principals Orthogonal Transforms (KLT Overview) Papers Review. Introduction. Two major classes of speech enhancement By modeling of noise/speech: like HMM
Speech Signal Representations I. Seminar Speech Recognition 2002 F.R. Verhage. Speech Signal Representations I. Decomposition of the speech signal (x[n]) as a source (e[n]) passed through a linear time-varying filter (h[n]). Speech Signal Representations I.
Speech Signal Processing I. By Edmilson Morais And Prof. Greg. Dogil Stuttgart, October 18, 2001. Goals of the Course. Our part Basic theoretical concepts about Speech Signal Processing - SDSP Waveform generation for TTS systems - TTS
Speech Information at Acoustic Landmarks. Mark Hasegawa-Johnson Electrical and Computer Engineering, UIUC. Outline of this Talk. Where is Speech Information? A Typology of Acoustic Landmarks Phoneme Encoding w/Distinctive Features Infograms Two-point Infograms