'Speech signal' presentation slideshows

Phonetic features in ASR

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Audio Features & Machine Learning

Audio Features & Machine Learning. E.M. Bakker . Features for Speech Recognition and Audio Indexing. Parametric Representations Short Time Energy Zero Crossing Rates Level Crossing Rates Short Time Spectral Envelope Spectral Analysis Filter Design Filter Bank Spectral Analysis Model

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683 views • 59 slides

Bellwork: The Ear

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Study on Speaker Recognition Based on HHT

Study on Speaker Recognition Based on HHT. ???? : ??? ?? ??? ?? ? ????? ? ?? 98/12/10. Outline. ?? abstract ?? Instantaneous frequency ?? EMD&IMF ?? speech signal pretreatment ?? Vector quantization ? ? conclusion ?? reference. abstract.

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Ken de Jong, Noah Silbert , Kirsten Regier & Aaron Albin IU & U Maryland

Statistical Relationships in Distinctive Feature Models and Acoustic-Phonetic Properties of English Consonants. Ken de Jong, Noah Silbert , Kirsten Regier & Aaron Albin IU & U Maryland. These slides posted at http:// www.indiana.edu/~lsl/ConsP.html. Traditional Consonant Representations.

392 views • 29 slides

Cellular Communications

Cellular Communications. 5. Speech Coding. Low Bit-rate Voice Coding. Voice is an analogue signal Needed to be transformed in a digital form (bits) Speech signal is not random=>can be encoded using fewer bits as compared to random signal

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CS 551/651: Structure of Spoken Language

CS 551/651: Structure of Spoken Language Lecture 1: Visualization of the Speech Signal, Introductory Phonetics John-Paul Hosom Fall 2010. Visualization of the Speech Signal Most common representations: Time-domain waveform Energy Pitch contour Spectrogram (power spectrum).

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Speech Conductor

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AIRUS (Automatic Information Retrieval Using Speech)

AIRUS (Automatic Information Retrieval Using Speech). Winter School `09-> Speech Track -> Group 4 Pulkit Shantanu Nitish Abhilash. Problem Formulation. N people are discussing in a group, each has a microphone Aim is to Remove the crosstalk in the speeches recorded by each microphone.

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Today

Today. What is Phonetics? Decoding the speech stream Principles of phonetic transcription IPA Readings: 3.1-3.2. Phonetics. The scientific study of human speech sounds How they are produced (articulatory) How they are perceived (auditory) Their physical properties (acoustic).

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CHAPTER 10 Applications of Digital Signal Processing

CHAPTER 10 Applications of Digital Signal Processing. Wang Weilian wlwang@ynu.edu.cn School of Information Science and Technology Yunnan University. Outline. Speech Signals Processing Dual-Tone Multifrequency Signal Detection. Speech Signals Processing. Speech Analysis

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Prosodic and Phonetic Features for Speaking Styles Classification and Detection

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Chapter 4: Advanced IR Models

Chapter 4: Advanced IR Models. 4.1 Probabilistic IR 4.2 Statistical Language Models (LMs) 4.2.1 Principles and Basic LMs 4.2.2 Smoothing Methods 4.2.3 Extended LMs 4.3 Latent-Concept Models. 4.2.1 What is a Statistical Language Model?. generative model for word sequence

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IP Cablecom and MEDIACOM 2004

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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.

343 views • 11 slides

Research ethics

Research ethics. Freedom , responsibility and universality if science. Freedom of movement , association, expression and communication for scientists Equitable access to data, information and research materials

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Basics of speech coding

Speech coding is the art of creating a minimally redundant representation of the speech signal that can be ef?ciently transmitted or stored in digital media, and decoding the signal with the best possible perceptual quality.Learn the art of speech coding. Enroll in Image and Video Communication Certificate Course by IIT Kharagpur.

223 views • 8 slides