Lecture1 - EE214A Digital Speech Processing Lecture 1 Abeer...

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1 EE214A Digital Speech Processing Lecture 1 Abeer Alwan all rights reserved Denes and Pinson (1963)
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2 Disciplines involved in Speech Research Engineering and Computer Science Linguistics (phonetics, phonology, semantics, syntax, etc.) Psychology, Biology, Neuroscience Audiology, Otolaryngology Some Speech R&D Areas/Apps. Mathematical models of production and perception mechanisms. Acoustics. Analysis, Text-to-Speech Synthesis (TTS), Compression, Enhancement, etc. Automatic Speech Recognition (ASR) Hearing aids Entertainment industry
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3 Linear Model of Speech Production Source Function Vocal Tract Speech Signal (periodic and/or noisy) Transfer Function (shown here in the Frequency Domain) x TTS Example ASR Systems Feature* Extraction Training Statistical Model Recognition Language Model Dictionary Text Output Speech Input *Typically, MFCCs
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4 Speech Recognition Accuracy Word Error Rate (WER) How good is an ASR system with 10 percent WER? http://www.hulu.com/watch/10409/saturday-night-live-julie-the-operator-lady
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5 Focus of this course STFT (used in most speech processing applications) LPC (used primarily in coding) Cepstral Analysis (used in automatic speech recognition ASR-) A follow up class, EE214B, focuses on speech coding and ASR Review 1. Frequency Transforms for: Continuous Time (CT) signals: Discrete Time (DT) signals: 2. Sampling Theorem 3. Up/down sampling 4. LTI systems and their properties 5. Basic circuit analysis techniques 6. Filters k c j X s X ), ( ), ( : k c k X j e X z X ), ( ), ( ), ( Z
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6 Transforms Note that all transforms are complex: (.) / (.) tan (.) (.) (.) | (.) | ) ( ) ( ) ( ) ( ) ( I 2 2 ) ( R I R I R j X X X X X jX X X e X X ± ± T T Continuous-Time (CT) Transforms 1) Laplace Transform: f j s dt e t x s X st S V 2 , ) ( ) ( : : ± ² ³
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7 2) Fourier Transform A powerful tool for describing the behavior of LTI systems.
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