Page 1 NOLISP, Paris, May 23rd 2007 Audio-Visual Audio-Visual Subspaces Audio Visual Reduced Audiovisual Subspace Principal Component & Linear Discriminant.

Slides:

Advertisements

Similar presentations

PF-STAR: emotional speech synthesis Istituto di Scienze e Tecnologie della Cognizione, Sezione di Padova – “Fonetica e Dialettologia”, CNR.

Advertisements

Improved ASR in noise using harmonic decomposition Introduction Pitch-Scaled Harmonic Filter Recognition Experiments Results Conclusion aperiodic contribution.

Robust Speech recognition V. Barreaud LORIA. Mismatch Between Training and Testing n mismatch influences scores n causes of mismatch u Speech Variation.

1 Analysis of Parameter Importance in Speaker Identity Ricardo de Córdoba, Juana M. Gutiérrez-Arriola Speech Technology Group Departamento de Ingeniería.

Audio Visual Speech Recognition

STQ Workshop, Sophia-Antipolis, February 11 th, 2003 Packet loss concealment using audio morphing Franck Bouteille¹ Pascal Scalart² Balazs Kövesi² ¹ PRESCOM.

Voiceprint System Development Design, implement, test unique voiceprint biometric system Research Day Presentation, May 3 rd 2013 Rahul Raj (Team Lead),

Speech & Audio Coding TSBK01 Image Coding and Data Compression Lecture 11, 2003 Jörgen Ahlberg.

Tangent Vectors and Normal Vectors. Definitions of Unit Tangent Vector.

High Level Prosody features: through the construction of a model for emotional speech Loic Kessous Tel Aviv University Speech, Language and Hearing

Prosody modification in speech signals Project by Edi Fridman & Alex Zalts supervision by Yizhar Lavner.

Pitch Prediction From MFCC Vectors for Speech Reconstruction Xu shao and Ben Milner School of Computing Sciences, University of East Anglia, UK Presented.

Abstract This article investigates the importance of the vocal source information for speaker recognition. We propose a novel feature extraction scheme.

Speaker Recognition Sharat.S.Chikkerur Center for Unified Biometrics and Sensors

Speech in Multimedia Hao Jiang Computer Science Department Boston College Oct. 9, 2007.

VOICE CONVERSION METHODS FOR VOCAL TRACT AND PITCH CONTOUR MODIFICATION Oytun Türk Levent M. Arslan R&D Dept., SESTEK Inc., and EE Eng. Dept., Boğaziçi.

Emotions and Voice Quality: Experiments with Sinusoidal Modeling Authors: Carlo Drioli, Graziano Tisato, Piero Cosi, Fabio Tesser Institute of Cognitive.

Robust Voice Activity Detection for Interview Speech in NIST Speaker Recognition Evaluation Man-Wai MAK and Hon-Bill YU The Hong Kong Polytechnic University.

SecurePhone Workshop - 24/25 June Speaking Faces Verification Kevin McTait Raphaël Blouet Gérard Chollet Silvia Colón Guido Aversano.

1 Cours parole du 9 Mars 2005 enseignants: Dr. Dijana Petrovska-Delacrétaz et Gérard Chollet Reconnaissance du locuteur 1.Introduction, Historique, Domaines.

Voice Transformation Project by: Asaf Rubin Michael Katz Under the guidance of: Dr. Izhar Levner.

The Chinese University of Hong Kong Department of Computer Science and Engineering Lyu0202 Advanced Audio Information Retrieval System.

Chapter 15 Speech Synthesis Principles 15.1 History of Speech Synthesis 15.2 Categories of Speech Synthesis 15.3 Chinese Speech Synthesis 15.4 Speech Generation.

Modern speech synthesis: communication aid personalisation Sarah Creer Stuart Cunningham Phil Green Clinical Applications of Speech Technology University.

A PRESENTATION BY SHAMALEE DESHPANDE

Human Psychoacoustics shows ‘tuning’ for frequencies of speech If a tree falls in the forest and no one is there to hear it, will it make a sound?

Text-To-Speech System for Marathi Miss. Deepa V. Kadam Indian Institute of Technology, Bombay.

Database Construction for Speech to Lip-readable Animation Conversion Gyorgy Takacs, Attila Tihanyi, Tamas Bardi, Gergo Feldhoffer, Balint Srancsik Peter.

Toshiba Update 04/09/2006 Data-Driven Prosody and Voice Quality Generation for Emotional Speech Zeynep Inanoglu & Steve Young Machine Intelligence Lab.

All features considered separately are relevant in a speech / music classification task. The fusion allows to raise the accuracy rate up to 94% for speech.

June 28th, 2004 BioSecure, SecurePhone 1 Automatic Speaker Verification : Technologies, Evaluations and Possible Future Gérard CHOLLET CNRS-LTCI, GET-ENST.

Research & development Learning optimal audiovisual phasing for an HMM-based control model for facial animation O. Govokhina (1,2), G. Bailly (2), G. Breton.

Prepared by: Waleed Mohamed Azmy Under Supervision:

Page 0 of 23 MELP Vocoders Nima Moghadam SN#: Saeed Nari SN#: Supervisor Dr. Saameti April 2005 Sharif University of Technology.

Hierarchical Dirichlet Process (HDP) A Dirichlet process (DP) is a discrete distribution that is composed of a weighted sum of impulse functions. Weights.

Page 1 Audiovisual Speech Analysis Ouisper Project - Silent Speech Interface.

17.0 Distributed Speech Recognition and Wireless Environment References: 1. “Quantization of Cepstral Parameters for Speech Recognition over the World.

Speech recognition and the EM algorithm

Signature with Text-Dependent and Text-Independent Speech for Robust Identity Verification B. Ly-Van*, R. Blouet**, S. Renouard** S. Garcia-Salicetti*,

Signature with Text-Dependent and Text-Independent Speech for Robust Identity Verification B. Ly-Van*, R. Blouet**, S. Renouard** S. Garcia-Salicetti*,

Speech Parameter Generation From HMM Using Dynamic Features Keiichi Tokuda, Takao Kobayashi, Satoshi Imai ICASSP 1995 Reporter: Huang-Wei Chen.

Speech Signal Processing I By Edmilson Morais And Prof. Greg. Dogil Second Lecture Stuttgart, October 25, 2001.

Dijana Petrovska-Delacrétaz 1 Asmaa el Hannani 1 Gérard Chollet 2 1: DIVA Group, University of Fribourg 2: GET-ENST, CNRS-LTCI,

AGA 4/28/ NIST LID Evaluation On Use of Temporal Dynamics of Speech for Language Identification Andre Adami Pavel Matejka Petr Schwarz Hynek Hermansky.

Korean Phoneme Discrimination Ben Lickly Motivation Certain Korean phonemes are very difficult for English speakers to distinguish, such as ㅅ and ㅆ.

Submitted By: Santosh Kumar Yadav (111432) M.E. Modular(2011) Under the Supervision of: Mrs. Shano Solanki Assistant Professor, C.S.E NITTTR, Chandigarh.

Overview ► Recall ► What are sound features? ► Feature detection and extraction ► Features in Sphinx III.

Speech Signal Processing I

MPEG-4 standard MPEG-4 Multimedia Standard Olivier Dechazal.

ECE 5525 Osama Saraireh Fall 2005 Dr. Veton Kepuska

ITU-T G.729 EE8873 Rungsun Munkong March 22, 2004.

Performance Comparison of Speaker and Emotion Recognition

Introduction Part I Speech Representation, Models and Analysis Part II Speech Recognition Part III Speech Synthesis Part IV Speech Coding Part V Frontier.

HMM-Based Speech Synthesis Erica Cooper CS4706 Spring 2011.

Present document contains informations proprietary to France Telecom. Accepting this document means for its recipient he or she recognizes the confidential.

Speech recognition Home Work 1. Problem 1 Problem 2 Here in this problem, all the phonemes are detected by using phoncode.doc There are several phonetics.

Chapter 20 Speech Encoding by Parameters 20.1 Linear Predictive Coding (LPC) 20.2 Linear Predictive Vocoder 20.3 Code Excited Linear Prediction (CELP)

1 Hidden Markov Model: Overview and Applications in MIR MUMT 611, March 2005 Paul Kolesnik MUMT 611, March 2005 Paul Kolesnik.

1 Voicing Features Horacio Franco, Martin Graciarena Andreas Stolcke, Dimitra Vergyri, Jing Zheng STAR Lab. SRI International.

A Hybrid Model of HMM and RBFN Model of Speech Recognition 길이만, 김수연, 김성호, 원윤정, 윤아림 한국과학기술원 응용수학전공.

Yow-Bang Wang, Lin-Shan Lee INTERSPEECH 2010 Speaker: Hsiao-Tsung Hung.

Mr. Darko Pekar, Speech Morphing Inc.

1 Vocoders. 2 The Channel Vocoder (analyzer) : The channel vocoder employs a bank of bandpass filters,  Each having a bandwidth between 100 HZ and 300.

Linear Predictive Coding Methods

Packet loss concealment using audio morphing

Presenter: Shih-Hsiang(士翔)

Measuring the Similarity of Rhythmic Patterns

Music Signal Processing

Auditory Morphing Weyni Clacken

Presentation transcript:

Page 1 NOLISP, Paris, May 23rd 2007 Audio-Visual Audio-Visual Subspaces Audio Visual Reduced Audiovisual Subspace Principal Component & Linear Discriminant Analysis x Correlated Audio & Visual Subspaces Co-inertia & Canonical Correlation Analysis

Page 2 NOLISP, Paris, May 23rd 2007 Voice conversion techniques Definition: Process of making one person’s voice « source » sounds like another person’s voice target source target Voice conversion My name is John

Page 3 NOLISP, Paris, May 23rd 2007 Principle of ALISP Dictionary of representative segments Spectral analysis Prosodic analysis Selection of segmental units Segment index Prosodic parameters Input speech Concatenative synthesis HNM Output speech CODER

Page 4 NOLISP, Paris, May 23rd 2007 details of Encoding speech Spectral analysis Prosodic analysis HMM Recognition Dictionary of HMM models of ALISP classes Synth unit A 1 … Synth unit A 8 HMM A Representative units of the class Selection by DTW Prosodic encoding Index of ALISP class Index of synth. unit Pitch, energy, duration

Page 5 NOLISP, Paris, May 23rd 2007 Details of decoding Output speech Synth unit A 1 … Synth unit A 8 ALISP Index Synth unit index within class Prosodic parameters Loading Synth unit Concatenative synthesis

Page 6 NOLISP, Paris, May 23rd 2007 Principle of Alisp conversion Learning step: one hour of target voice - Parametric analysis: MFCC - Segmentation based on temporal decompostion and vector quantization - Stochastic modelling based on HMM - Creation of representative units Conversion step - Parametric analysis: MFCC - HMM recognition - Selection of representative segment  DTW Synthesis step - Concatenation of representative - HNM synthesis

Page 7 NOLISP, Paris, May 23rd 2007 Voice conversion using ALISP results Score distributionDET curve EER before forgery: 16 % (1729 impostors, 1320 clients) EER after forgery : 26 % (1729 impostors, 1320 clients)

Page 8 NOLISP, Paris, May 23rd 2007 Voice conversion using ALISP results BREF databaseNIST database Source Result Target SourceTarget Result female male