'Linear prediction' diaporamas de présentation

LSA 352: Speech Recognition and Synthesis

LSA 352: Speech Recognition and Synthesis. Dan Jurafsky Lecture 1: 1) Overview of Course 2) Refresher: Intro to Probability 3) Language Modeling. IP notice: some slides for today from: Josh Goodman, Dan Klein, Bonnie Dorr, Julia Hirschberg, Sandiway Fong. Outline.

1.12k views • 101 slides

Signal Modeling for Robust Speech Recognition With Frequency Warping and Convex Optimization

Signal Modeling for Robust Speech Recognition With Frequency Warping and Convex Optimization. Yoon Kim March 8, 2000. Outline. Introduction and Motivation Speech Analysis with Frequency Warping Speaker Normalization with Convex Optimization Experimental Results Conclusions.

829 views • 67 slides

Data input

Data input. STATA format EXCEL format Other format. STATA format. Excel format. Other format. Graph. Scatter Linear prediction. Regression. Linear regression ( homoscedasticity ) Linear regression with robust standard errors. Linear regression.

851 views • 65 slides

A 12-WEEK PROJECT IN Speech Coding and Recognition

A 12-WEEK PROJECT IN Speech Coding and Recognition. by Fu-Tien Hsiao and Vedrana Andersen. Overview. An Introduction to Speech Signals (Vedrana) Linear Prediction Analysis (Fu) Speech Coding and Synthesis (Fu) Speech Recognition (Vedrana). Speech Coding and Recognition.

692 views • 53 slides

VoIP Conception and Implementation

740 views • 44 slides

LINEAR PREDICTION

LINEAR PREDICTION. Linear Prediction . Problem: Forward Prediction Observing Predict Backward Prediction Observing Predict. Forward Linear Prediction. Problem: Forward Prediction Observing the past Predict the future i.e. find the predictor filter taps w f,1 , w f,2 ,...,w f,M. ?.

891 views • 38 slides

By: Kendall Khodra Instructor: Dr. Kepuska

A Comparison Of Speech Coding With Linear Predictive Coding (LPC) And Code-Excited Linear Predictor Coding (CELP). By: Kendall Khodra Instructor: Dr. Kepuska. Introduction This project will develop Linear Predictive Coding (LPC) to process a speech signal. The objective is to mitigate

506 views • 35 slides

Speech Coding

Speech Coding. Submitted To: Dr. Mohab Mangoud Submitted By: Nidal Ismail. Outline. Introduction Overview of Speech Coding Properties of a Speech Coder Modeling the Speech Production System Linear Prediction Different Coding Techniques Waveform Coders Parametric Coders Hybrid Coders

2.18k views • 34 slides

1 st and 2 nd Generation Synthesis

1 st and 2 nd Generation Synthesis. Speech Synthesis Generation First: Ground Up Synthesis Second: Data Driven Synthesis by Concatenation Input (Sequence of) Phonetic symbols Duration F0 contours Amplification factors Data Rule-based parameters

455 views • 26 slides

Genomic Prediction Results

Genomic Prediction Results. Measuring Genetic Similarity. Cattle genome sequenced in 2004 30 chromosome pairs (including X,Y) 3 billion letters from each parent Illumina Bovine SNP50 TM Chip 58,000 genetic markers in 2007 39,835 used in genomic predictions Cost about $200 per animal.

408 views • 25 slides

Genomic Prediction Results

Genomic Prediction Results. Measuring Genetic Similarity. Cattle genome sequenced in 2004 30 chromosome pairs (including X,Y) 3 billion letters from each parent Illumina Bovine SNP50 TM Chip 58,000 genetic markers in 2007 39,835 used in genomic predictions Cost about $200 per animal.

421 views • 25 slides

AN IMPROVED AUDIO

AN IMPROVED AUDIO. Jenn Tam jdtam@cs.cmu.edu Computer Science Dept. Carnegie Mellon University SOAPS 2008, Pittsburgh, PA. WHAT ARE CAPTCHAS?. CAPTCHAs are tests generated by computers and generally passable by humans but not current computer programs.

495 views • 22 slides

Internet Telephony

Internet Telephony. Vincent Declercq MSIT 526: Fall 2005 19 December 2005. Outline. History Deployment Circuit Switching Packet Switching Hardware Software Advantages & Disadvantages Pricing Future of VoIP. History. 1836: telegraph 1 st breakthrough in human telecommunication

387 views • 21 slides

Welfare implications of subsidization in the Dutch housing market

Welfare implications of subsidization in the Dutch housing market. Frans Schilder. 24-06-2010. Motive. Abundance of subsidies and little study into its effect (i.e. in Dutch context) Compare recent findings using procedure as Rosen (1979) and Poterba (1992)

319 views • 20 slides

Genomic Selection in Dairy Cattle

Genomic Selection in Dairy Cattle. Simulated Results World Bull Population. 15,197 older and 5,987 younger bulls in Interbull file 40,000 SNPs and 10,000 QTLs Provided timing, memory test Reliability vs parent average REL REL = corr 2 (EBV, true BV) 80% vs 34% expected for young bulls

601 views • 15 slides

Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction

Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction. By: Fei Nan & Hani Saad Presented to: Dr. Donald Adjeroh. Index. Hyperspectral Images, what are they? Remote Sensors and Low-complexity Image Compression Linear Prediction (LP)

349 views • 14 slides

Warped Linear Prediction

Warped Linear Prediction. Concept : Warp the spectrum to emulate human perception; then perform linear prediction on the result Approaches to warp the spectrum: Fourier transform, warp, and transform back Bank of overlapping band-pass filters. We seen this in one of the VAD algorithms

281 views • 13 slides

Linear prediction

Linear prediction. Linear prediction is a method used to reduce the bandwidth required to transmit PCM pulses It is widely used in speech communications over mobile channels In linear prediction the future values of a discrete time signals are estimated as a linear function of previous samples.

345 views • 13 slides

Linear Prediction

Linear Prediction. Linear Prediction (Introduction) :. The object of linear prediction is to estimate the output sequence from a linear combination of input samples, past output samples or both : The factors a(i) and b(j) are called predictor coefficients.

392 views • 11 slides

Linear Prediction

Linear Prediction. Linear Prediction (Introduction) :. The object of linear prediction is to estimate the output sequence from a linear combination of input samples, past output samples or both : The factors a(i) and b(j) are called predictor coefficients.

233 views • 11 slides