IN2014DN10400A - - Google Patents

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Publication number
IN2014DN10400A
IN2014DN10400A IN10400DEN2014A IN2014DN10400A IN 2014DN10400 A IN2014DN10400 A IN 2014DN10400A IN 10400DEN2014 A IN10400DEN2014 A IN 10400DEN2014A IN 2014DN10400 A IN2014DN10400 A IN 2014DN10400A
Authority
IN
India
Prior art keywords
signal
analysis module
probabilities
analyzing
basis
Prior art date
Application number
Inventor
Bartosz ZIÓLKO
Tomasz Jadczyk
Original Assignee
Akademia Górniczo Hutnicza Im Stanislawa Staszica W Krakowie
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Akademia Górniczo Hutnicza Im Stanislawa Staszica W Krakowie filed Critical Akademia Górniczo Hutnicza Im Stanislawa Staszica W Krakowie
Publication of IN2014DN10400A publication Critical patent/IN2014DN10400A/en

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/16—Speech classification or search using artificial neural networks
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/04—Segmentation; Word boundary detection
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/14—Speech classification or search using statistical models, e.g. Hidden Markov Models [HMMs]
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/24—Speech recognition using non-acoustical features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Multimedia (AREA)
  • Probability & Statistics with Applications (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Machine Translation (AREA)
  • Telephonic Communication Services (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

A computer implemented method for speech recognition comprising the steps of: registering (201) by means of an input device (102A) electrical signal representing speech and converting the signal to frequency or time frequency domain (202) analyzing the signal in an analysis module based on Dynamic Bayesian Network (205) configured to generate hypotheses of words (W) and their probabilities on the basis of observed signal features (OA OV) recognizing (209) a text corresponding to the electrical signal representing speech on the basis of certain word (W) hypotheses and their probabilities. The method is characterized by inputting to the analysis module (205) observed signal features (308 312) which are determined for the signal in frequency or time frequency domain (202) in at least two parallel signal processing lines (204a 204b 204c 204d 201a) for time segments distinct for each line and analyzing in the analysis module (205) relations between observed signal features (308 312) for at least two distinct time segments in the analysis module (205).
IN10400DEN2014 2013-05-01 2013-06-26 IN2014DN10400A (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
PL403724A PL403724A1 (en) 2013-05-01 2013-05-01 Speech recognition system and a method of using dynamic models and Bayesian networks
PCT/EP2013/063330 WO2014177232A1 (en) 2013-05-01 2013-06-26 A speech recognition system and a method of using dynamic bayesian network models

Publications (1)

Publication Number Publication Date
IN2014DN10400A true IN2014DN10400A (en) 2015-08-14

Family

ID=48699782

Family Applications (1)

Application Number Title Priority Date Filing Date
IN10400DEN2014 IN2014DN10400A (en) 2013-05-01 2013-06-26

Country Status (9)

Country Link
US (1) US9552811B2 (en)
EP (1) EP2959475B1 (en)
JP (1) JP2016517047A (en)
CN (1) CN104541324B (en)
AU (1) AU2013388411A1 (en)
CA (1) CA2875727A1 (en)
IN (1) IN2014DN10400A (en)
PL (2) PL403724A1 (en)
WO (1) WO2014177232A1 (en)

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US10043521B2 (en) 2016-07-01 2018-08-07 Intel IP Corporation User defined key phrase detection by user dependent sequence modeling
CN106297828B (en) * 2016-08-12 2020-03-24 苏州驰声信息科技有限公司 Detection method and device for false sounding detection based on deep learning
US10083689B2 (en) * 2016-12-23 2018-09-25 Intel Corporation Linear scoring for low power wake on voice
WO2018209608A1 (en) * 2017-05-17 2018-11-22 Beijing Didi Infinity Technology And Development Co., Ltd. Method and system for robust language identification
US10902738B2 (en) * 2017-08-03 2021-01-26 Microsoft Technology Licensing, Llc Neural models for key phrase detection and question generation
CN107729381B (en) * 2017-09-15 2020-05-08 广州嘉影软件有限公司 Interactive multimedia resource aggregation method and system based on multi-dimensional feature recognition
US10714122B2 (en) 2018-06-06 2020-07-14 Intel Corporation Speech classification of audio for wake on voice
US10650807B2 (en) 2018-09-18 2020-05-12 Intel Corporation Method and system of neural network keyphrase detection
US11127394B2 (en) 2019-03-29 2021-09-21 Intel Corporation Method and system of high accuracy keyphrase detection for low resource devices
CN110838306B (en) * 2019-11-12 2022-05-13 广州视源电子科技股份有限公司 Voice signal detection method, computer storage medium and related equipment
US20220036087A1 (en) * 2020-07-29 2022-02-03 Optima Sports Systems S.L. Computing system and a computer-implemented method for sensing events from geospatial data
CN114612810B (en) * 2020-11-23 2023-04-07 山东大卫国际建筑设计有限公司 Dynamic self-adaptive abnormal posture recognition method and device
CN115718536B (en) * 2023-01-09 2023-04-18 苏州浪潮智能科技有限公司 Frequency modulation method and device, electronic equipment and readable storage medium

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US6292776B1 (en) 1999-03-12 2001-09-18 Lucent Technologies Inc. Hierarchial subband linear predictive cepstral features for HMM-based speech recognition
US6542866B1 (en) * 1999-09-22 2003-04-01 Microsoft Corporation Speech recognition method and apparatus utilizing multiple feature streams
US7346510B2 (en) * 2002-03-19 2008-03-18 Microsoft Corporation Method of speech recognition using variables representing dynamic aspects of speech
US20030212552A1 (en) * 2002-05-09 2003-11-13 Liang Lu Hong Face recognition procedure useful for audiovisual speech recognition
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US7454342B2 (en) * 2003-03-19 2008-11-18 Intel Corporation Coupled hidden Markov model (CHMM) for continuous audiovisual speech recognition
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Also Published As

Publication number Publication date
EP2959475A1 (en) 2015-12-30
CN104541324A (en) 2015-04-22
US9552811B2 (en) 2017-01-24
CN104541324B (en) 2019-09-13
CA2875727A1 (en) 2014-11-06
JP2016517047A (en) 2016-06-09
US20160111086A1 (en) 2016-04-21
PL2959475T3 (en) 2018-04-30
EP2959475B1 (en) 2017-02-08
WO2014177232A1 (en) 2014-11-06
PL403724A1 (en) 2014-11-10
AU2013388411A1 (en) 2015-01-22

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