'Acoustic model' diaporamas de présentation

Acoustic Databases

1.48k views • 133 slides

Automatic Dialect/Accent Recognition

Automatic Dialect/Accent Recognition. Fadi Biadsy April 12 th , 2010. Outline. Problem Motivation Corpora Framework for Language Recognition Experiments in Dialect Recognition Phonotactic Modeling Prosodic Modeling Acoustic Modeling Discriminative Phonotactics .

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Automatic Speech Recognition: An Overview

Automatic Speech Recognition: An Overview. Julia Hirschberg CS 4706 (Thanks to Roberto Pieraccini and Francis Ganong for some slides). DIALOG. SEMANTICS. SPEECH RECOGNITION. SPOKEN LANGUAGE UNDERSTANDING. SYNTAX. LEXICON. MORPHOLOGY. SPEECH SYNTHESIS. PHONETICS. DIALOG

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Large scale discriminative training for speech recognition

Large scale discriminative training for speech recognition. P.C. Woodland and D. Povey Cambridge University Engineering Department 2000 present by ??. Outline. Introduction MMIE criterion Extended Baum-Welch algorithm

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Digital Media

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Robust Automatic Speech Recognition by Transforming Binary Uncertainties

Robust Automatic Speech Recognition by Transforming Binary Uncertainties. DeLiang Wang (Jointly with Dr. Soundar Srinivasan) Oticon A/S, Denmark (On leave from Ohio State University, USA). Outline of presentation. Background The robustness problem in automatic speech recognition (ASR)

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Hidden Markov Model in Automatic Speech Recognition

Hidden Markov Model in Automatic Speech Recognition. Z. Fodroczi Pazmany Peter Catholic. Univ. Outline. Discrete Markov process Hidden Markov Model Vitterbi algorithm Forward algorithm Parameter estimation Type of them HMMs in ASR Systems Popular models State of the art and limitation.

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Spoken Dialog Systems

Spoken Dialog Systems. Lecture 3 Spoken Language Processing Prof. Andrew Rosenberg. Overview. Conversational Agents Automatic Speech Recognition (ASR) Natural Language Understanding (NLU) Generation Cooperative Question-Answering Dialog Representations. Spoken Dialog Systems.

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Digital Speech Processing HW3

Digital Speech Processing HW3. ?? ? ??? 2013/11/20. Outline. Introduction SRILM Requirement Submission Format. Outline. Introduction SRILM Requirement Submission Format. Introduction. ? ? ?? ?? ? ?? ?? ?? ? ? ? ?? ?? ? ?? ?? ?? ?? ?? ?? ?? ???. Your HW3. ? ? ?? ??

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DHMC Cardiology Update 2004

DHMC Cardiology Update 2004. December 6, 2004. Speech Recognition in Medical Practice. Jon W. Wahrenberger, MD. Medical Documentation. Old Paradigm: hand-written documentation. Jury blames doctor's bad penmanship for patient death Award = $475,000. Medical Documentation.

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Discriminative Learning for Hidden Markov Models

Discriminative Learning for Hidden Markov Models. Li Deng. Microsoft Research. EE 516; UW Spring 2009. Minimum Classification Error (MCE). The objective function of MCE training is a smoothed recognition error rate.

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Less is More?

Less is More?. Yi Wu Advisor: Alex Rudnicky. People:. There is no data like more data!. Goal: Use less to Perform more. Identifying an informative subset from a large corpus for Acoustic Model (AM) training. Expectation of the Selected Set Good in Performance Fast in Selection.

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Acoustic / Lexical Model

Acoustic / Lexical Model. Derk Geene. Speech recognition. P(words|signal)= P(signal|words) P(words) / P(signal) P(signal|words): Acoustic model P(words): Language model Idea: Maximize P(signal|words) P(words) Today: Acoustic model. Variability. Variation Speaker Pronunciation

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Better Recognition by manipulation of ASR results

Better Recognition by manipulation of ASR results. Generic concepts for post computation recognizer result components. Emmett Coin Industrial Poet. ejTalk, Inc. www.ejTalk.com. Who?. Emmett Coin Industrial Poet Rugged solutions via compact and elegant techniques

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???? (2) HINIT + HREST + HEREST

???? (2) HINIT + HREST + HEREST. Prof. Lin-Shan Lee, TA. Yun-Nung Chen. Acoustic Model Initialization. HInit + HRest. Feature Extraction - MFCC. Example of MFCC. Acoustic Model. 5. Hidden Markov Model/Gaussian Mixture Model Acoustic model unit : Initial Final set 3 states per model

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Goals : Down sample 20 khz TIDigits data to 16 khz .

Goals : Down sample 20 khz TIDigits data to 16 khz . 2. Use Down sample data run regression test and Compare results posted in Sphinx-4 website 3. Based on the results, make decisions (issue with microprocessor, floating point etc .) By Jaykrishna shukla , Mubin Ahmed and Cara Santin.

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Acoustic models in Kaldi

Acoustic models in Kaldi . Acoustic models in Kaldi . Support for standard ML-trained models Linear transforms like LDA, HLDA, MLLT/STC Speaker adaptation with fMLLR, MLLR Support for tied-mixture systems initially discussed Support for SGMMs

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CS 6998 Computational Approach to Emotional Speech Instructor: Prof. Julia Hirschberg

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???? (3) Viterbi Decoding Triphone Acoustic Model

???? (3) Viterbi Decoding Triphone Acoustic Model. Prof. Lin-Shan Lee, TA. Yun-Chiao Li. Viterbi Decoding. 03.04.mono0a.viterbi.sh 04.04.tri1.viterbi.sh. Viterbi Decoding. Instead of using WFST, we use Viterbi now Converted Kaldi Acoustic model to HTK by Vulcan (02.02.convert.htk.feat.sh).

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Given a query (text or audio ) Detection the query within target audio

Explorations in Zero-Resource Spoken Term Detection Justin Chiu Language Technology Institute, Carnegie Mellon University. What is STD?. Preliminary Results. Given a query (text or audio ) Detection the query within target audio Common Approach: Recognized -> Search.

129 views • 1 slides