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- ABORTONERR
- 4.5 Error Reporting
- accumulators
- 1.4 Baum-Welch Re-Estimation, 8.5 Embedded Training using HEREST
- accuracy figure
- 3.4.1 Step 11 - Recognising the Test Data
- ACCWINDOW
- 5.6 Delta and Acceleration Coefficients
- ADDDITHER
- 5.2 Speech Signal Processing
- ALIEN
- 5.8.11 ALIEN and NOHEAD File Formats
- all-pole filter
- 5.3 Linear Prediction Analysis
- ALLOWCXTEXP
- 11.8 Word Network Expansion
- ALLOWXWRDEXP
- 3.4.1 Step 11 - Recognising the Test Data, 11.8 Word Network Expansion
- analysis
- FFT-based
- 3.1.5 Step 5 - Coding the Data
- LPC-based
- 3.1.5 Step 5 - Coding the Data
- ANON
- 5.1 General Mechanism
- AT command
- 3.2.2 Step 7 - Fixing the Silence Models, 9.7 Miscellaneous Operations
- AU command
- 3.3.2 Step 10 - Making Tied-State Triphones, 3.3.2 Step 10 - Making Tied-State Triphones, 3.5 Running the Recogniser Live, 9.5 Tree-Based Clustering
- audio output
- 5.9 Direct Audio Input/Output, 5.9 Direct Audio Input/Output
- audio source
- 5.9 Direct Audio Input/Output
- AUDIOSIG
- 5.9 Direct Audio Input/Output
- average log probability
- 12.3 Recognition using Test Databases
- back-off bigrams
- 11.4 Bigram Language Models
- ARPA MIT-LL format
- 11.4 Bigram Language Models
- backward probability
- 1.4 Baum-Welch Re-Estimation
- Baum-Welch algorithm
- 1.4 Baum-Welch Re-Estimation
- Baum-Welch re-estimation
- 1.4 Baum-Welch Re-Estimation, 1.4 Baum-Welch Re-Estimation
- embedded unit
- 8.5 Embedded Training using HEREST
- isolated unit
- 8.4 Isolated Unit Re-Estimation using HREST
- Bayes' Rule
- 1.2 Isolated Word Recognition
- beam width
- 8.5 Embedded Training using HEREST, 12.1 Decoder Operation
- <BeginHMM>
- 7.2 Basic HMM Definitions
- binary chop
- 10.4 Parameter Smoothing
- binary storage
- 7.8 Binary Storage Format, 9.2 Constructing Context-Dependent Models
- binning
- 5.4 Filterbank Analysis
- Boolean values
- 4.3 Configuration Files
- bootstrapping
- 1.6 Continuous Speech Recognition, 2.3.2 Training Tools, 8.1 Training Strategies
- byte swapping
- 4.9 Byte-swapping of HTK data files, 5.2 Speech Signal Processing
- byte-order
- 5.2 Speech Signal Processing
- BYTEORDER
- 5.2 Speech Signal Processing
- C-heaps
- 4.7 Memory Management
- CEPLIFTER
- 5.3 Linear Prediction Analysis, 5.4 Filterbank Analysis
- cepstral analysis
- filter bank
- 5.4 Filterbank Analysis
- liftering coefficient
- 5.3 Linear Prediction Analysis
- LPC based
- 5.3 Linear Prediction Analysis
- power vs magnitude
- 5.4 Filterbank Analysis
- cepstral coefficients
- liftering
- 5.3 Linear Prediction Analysis
- cepstral mean normalisation
- 5.4 Filterbank Analysis
- CFWORDBOUNDARY
- 11.8 Word Network Expansion
- CH command
- 6.4 Editing Label Files
- check sums
- 5.13 Copying and Coding using HCOPY
- CHKHMMDEFS
- 7.2 Basic HMM Definitions
- Choleski decomposition
- 7.2 Basic HMM Definitions
- CL command
- 3.3.1 Step 9 - Making Triphones from Monophones, 9.2 Constructing Context-Dependent Models
- cloning
- 3.3.1 Step 9 - Making Triphones from Monophones, 3.3.1 Step 9 - Making Triphones from Monophones, 9.2 Constructing Context-Dependent Models
- cluster merging
- 3.3.2 Step 10 - Making Tied-State Triphones
- clustering
- data-driven
- 9.4 Data-Driven Clustering
- tracing in
- 9.5 Tree-Based Clustering
- tree-based
- 9.5 Tree-Based Clustering
- CO command
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.4 Data-Driven Clustering
- codebook
- 7.6 Discrete Probability HMMs
- codebook exponent
- 1.3 Output Probability Specification
- codebooks
- 1.3 Output Probability Specification
- coding
- 3.1.5 Step 5 - Coding the Data
- command line
- arguments
- 3.1.5 Step 5 - Coding the Data, 4.1 The Command Line
- arguments
- 3.1.5 Step 5 - Coding the Data, 4.1 The Command Line
- ellipsed arguments
- 4.2 Script Files
- integer argument formats
- 4.1 The Command Line
- options
- 2.2 Generic Properties of a HTK Tool, 4.1 The Command Line
- options
- 2.2 Generic Properties of a HTK Tool, 4.1 The Command Line
- script files
- 3.1.5 Step 5 - Coding the Data
- command line options
- 2.4 Whats New in Version 2.0?
- compression
- 5.13 Copying and Coding using HCOPY
- configuration files
- 2.2 Generic Properties of a HTK Tool, (, )
- default
- 4.3 Configuration Files
- display
- 4.4 Standard Options
- format
- 4.3 Configuration Files
- types
- 4.3 Configuration Files
- configuration parameters
- operating;tex2html_html_special_mark_quot;environment
- 4.10 Summary
- switching
- 8.6 Single-Pass Retraining
- configuration variables
- 2.1 HTK Software Architecture
- display of
- 4.3 Configuration Files
- summary
- 14 Configuration Variables
- confusion matrix
- 12.4 Evaluating Recognition Results
- context dependent models
- 9.2 Constructing Context-Dependent Models
- continuous speech recognition
- 8.1 Training Strategies
- covariance matrix
- 7.1 The HMM Parameters
- cross-word network expansion
- 11.8 Word Network Expansion
- cross-word triphones
- 9.2 Constructing Context-Dependent Models
- data insufficiency
- 3.3.2 Step 10 - Making Tied-State Triphones
- data preparation
- 2.3.1 Data Preparation Tools, 3.1 Data Preparation
- DC command
- 6.4 Editing Label Files, 11.7 Constructing a Dictionary
- DE command
- 6.4 Editing Label Files
- decision tree-based clustering
- 9.5 Tree-Based Clustering
- decision trees
- 3.3.2 Step 10 - Making Tied-State Triphones
- loading and storing
- 9.5 Tree-Based Clustering
- decoder
- 12 Decoding
- alignment mode
- 12.2 Decoder Organisation
- evaluation
- 12.3 Recognition using Test Databases
- forced alignment
- 12.5 Generating Forced Alignments
- live input
- 12.6 Recognition using Direct Audio Input
- N-best
- 12.7 N-Best Lists and Lattices
- operation
- 12.1 Decoder Operation
- organisation
- 12.2 Decoder Organisation
- output formatting
- 12.5 Generating Forced Alignments
- output MLF
- 12.3 Recognition using Test Databases
- progress reporting
- 12.3 Recognition using Test Databases
- recognition mode
- 12.2 Decoder Organisation
- rescoring mode
- 12.2 Decoder Organisation
- results analysis
- 12.4 Evaluating Recognition Results
- trace output
- 12.3 Recognition using Test Databases
- decompression;tex2html_html_special_mark_quot;filter
- 4.8 Input/Output via Pipes and Networks
- defunct mixture components
- 8.4 Isolated Unit Re-Estimation using HREST
- defunct mixtures
- 9.6 Mixture Incrementing
- deleted interpolation
- 10.4 Parameter Smoothing
- deletion errors
- 12.3 Recognition using Test Databases
- delta coefficients
- 5.6 Delta and Acceleration Coefficients
- DELTAWINDOW
- 5.6 Delta and Acceleration Coefficients
- dictionaries
- 11 NetworksDictionaries and Language Models
- dictionary
- construction
- 3.1.2 Step 2 - the Dictionary, 11.7 Constructing a Dictionary
- construction
- 3.1.2 Step 2 - the Dictionary, 11.7 Constructing a Dictionary
- edit commands
- 11.7 Constructing a Dictionary
- entry
- 3.1.2 Step 2 - the Dictionary
- format
- 3.1.2 Step 2 - the Dictionary
- formats
- 11.7 Constructing a Dictionary
- output symbols
- 11.7 Constructing a Dictionary
- digit recogniser
- 11.3 Building a Word Network with HPARSE
- direct audio input
- 5.9 Direct Audio Input/Output
- DISCOUNT
- 11.4 Bigram Language Models
- DISCRETE
- 5.11 Vector Quantisation
- discrete HMM
- output probability scaling
- 7.6 Discrete Probability HMMs
- discrete HMMs
- 7.6 Discrete Probability HMMs, 10 Discrete and Tied-Mixture Models
- discrete probability
- 7.1 The HMM Parameters, 7.6 Discrete Probability HMMs
- discrete;tex2html_html_special_mark_quot;data
- 10.1 Modelling Discrete Sequences
- DISCRETEHS
- 7.4 HMM Sets
- DP command
- 9.7 Miscellaneous Operations
- duration parameters
- 7.1 The HMM Parameters
- duration vector
- 7.9 The HMM Definition Language
- EBNF
- 2.3.3 Recognition Tools, 11.3 Building a Word Network with HPARSE
- edit commands
- single letter
- 6.4 Editing Label Files
- edit file
- 6.4 Editing Label Files
- embedded re-estimation
- 3.2.1 Step 6 - Creating Flat Start Monophones
- embedded training
- 1.6 Continuous Speech Recognition, 2.3.2 Training Tools, 8.1 Training Strategies, 8.5 Embedded Training using HEREST
- <EndHMM>
- 7.2 Basic HMM Definitions
- energy suppression
- 5.10 Multiple Input Streams
- ENORMALISE
- 4.3 Configuration Files, 5.5 Energy Measures
- environment variables
- 4.10 Summary
- error message
- format
- 15 Error and Warning Codes
- error number
- structure of
- 15 Error and Warning Codes
- error numbers
- structure of
- 4.5 Error Reporting
- errors
- 4.5 Error Reporting
- full listing
- 15 Error and Warning Codes
- ESCALE
- 5.5 Energy Measures
- Esignal
- 2.4 Whats New in Version 2.0?
- ESPS files
- 2.4 Whats New in Version 2.0?
- EX command
- 6.2.4 SCRIBE Label Files, 12.5 Generating Forced Alignments
- extended Backus-Naur Form
- 11.3 Building a Word Network with HPARSE
- extensions
- mfc
- 3.1.5 Step 5 - Coding the Data
- scp
- 3.1.5 Step 5 - Coding the Data
- wav
- 3.1.3 Step 3 - Recording the Data
- Figure of Merit
- 2.3.4 Analysis Tool, 12.4 Evaluating Recognition Results
- file formats
- 2.4 Whats New in Version 2.0?
- ALIEN
- 5.8.11 ALIEN and NOHEAD File Formats
- Audio Interchange (AIFF)
- 5.8.7 AIFF File Format
- Esignal
- 5.7.2 Esignal Format Parameter Files, 5.8.2 Esignal File Format
- Esignal
- 5.7.2 Esignal Format Parameter Files, 5.8.2 Esignal File Format
- HTK
- 5.8.1 HTK File Format
- NIST
- 5.8.4 NIST File Format
- NOHEAD
- 5.8.11 ALIEN and NOHEAD File Formats
- OGI
- 5.8.9 OGI File Format
- SCRIBE
- 5.8.5 SCRIBE File Format
- Sound Designer(SDES1)
- 5.8.6 SDES1 File Format
- Sun audio (SUNAU8)
- 5.8.8 SUNAU8 File Format
- TIMIT
- 5.8.3 TIMIT File Format
- WAVE
- 5.8.10 WAVE File Format
- file;tex2html_html_special_mark_quot;formats
- HTK
- 5.7.1 HTK Format Parameter Files
- files
- adding checksums
- 5.13 Copying and Coding using HCOPY
- compressing
- 5.13 Copying and Coding using HCOPY
- configuration
- 4.3 Configuration Files
- copying
- 5.13 Copying and Coding using HCOPY
- listing contents
- 5.12 Viewing Speech with HLIST
- network problems
- 4.8 Input/Output via Pipes and Networks
- opening
- 4.8 Input/Output via Pipes and Networks
- script
- 4.2 Script Files
- VQ codebook
- 5.11 Vector Quantisation
- filters
- 4.8 Input/Output via Pipes and Networks
- fixed-variance
- 8.3 Flat Starting with HCOMPV
- flat start
- 2.3.2 Training Tools, 3.1.4 Step 4 - Creating the Transcription Files, 3.2.1 Step 6 - Creating Flat Start Monophones, 8.1 Training Strategies, 8.3 Flat Starting with HCOMPV
- float values
- 4.3 Configuration Files
- FOM
- 2.3.4 Analysis Tool, 12.4 Evaluating Recognition Results
- FORCECXTEXP
- 3.4.1 Step 11 - Recognising the Test Data, 11.8 Word Network Expansion
- forced alignement
- 12.5 Generating Forced Alignments
- forced alignment
- 1.6 Continuous Speech Recognition
- forced recognition
- 3.2 Creating Monophone HMMs
- FORCELEFTBI
- 11.8 Word Network Expansion
- FORCEOUT
- 12.3 Recognition using Test Databases
- FORCERIGHTBI
- 11.8 Word Network Expansion
- forward probability
- 1.4 Baum-Welch Re-Estimation
- forward-backward
- embedded
- 8.5 Embedded Training using HEREST
- isolated unit
- 8.4 Isolated Unit Re-Estimation using HREST
- Forward-Backward algorithm
- 1.4 Baum-Welch Re-Estimation
- full rank covariance
- 7.2 Basic HMM Definitions
- Gaussian mixture
- 1.3 Output Probability Specification
- Gaussian pre-selection
- 5.11 Vector Quantisation
- generalised triphones
- 9.4 Data-Driven Clustering
- global.ded
- 11.7 Constructing a Dictionary
- global options
- 7.9 The HMM Definition Language
- global options macro
- 8.2 Initialisation using HINIT
- global speech variance
- 8.1 Training Strategies
- grammar
- 11.3 Building a Word Network with HPARSE
- grammar scale factor
- 3.4.1 Step 11 - Recognising the Test Data
- grand variance
- 9.3 Parameter Tying and Item Lists
- HALIGN(1.5)
- 2.4 Whats New in Version 2.0?
- Hamming Window
- 5.2 Speech Signal Processing
- HAUDIO
- 2.1 HTK Software Architecture, 5.9 Direct Audio Input/Output
- HBUILD
- 2.3.3 Recognition Tools, 11.4 Bigram Language Models, 11.5 Building a Word Network with HBUILD, (, )
- HCODE(1.5)
- 2.4 Whats New in Version 2.0?
- HCOMPV
- 2.3.2 Training Tools, 3.2.1 Step 6 - Creating Flat Start Monophones, 8.1 Training Strategies, 8.3 Flat Starting with HCOMPV, (, )
- HCONFIG
- 4.3 Configuration Files
- HCOPY
- 2.3.1 Data Preparation Tools, 3.1.5 Step 5 - Coding the Data, 4.2 Script Files, 5.13 Copying and Coding using HCOPY, 10.2 Using Discrete Models with Speech, (, )
- HDICT
- 2.1 HTK Software Architecture
- HDMAN
- 2.3.3 Recognition Tools, 3.1.2 Step 2 - the Dictionary, 11.7 Constructing a Dictionary, (, )
- HEADERSIZE
- 5.8.11 ALIEN and NOHEAD File Formats
- HEREST
- 1.6 Continuous Speech Recognition, 2.3.2 Training Tools, 3.2.1 Step 6 - Creating Flat Start Monophones, 8.1 Training Strategies, 8.5 Embedded Training using HEREST, 9.1 Using HHED, (, )
- HGRAF
- 2.1 HTK Software Architecture
- HHED
- 2.3.2 Training Tools, 3.2.2 Step 7 - Fixing the Silence Models, 3.5 Running the Recogniser Live, 8.1 Training Strategies, 9 HMM System Refinement, (, )
- HINIT
- 1.4 Baum-Welch Re-Estimation, 1.4 Baum-Welch Re-Estimation, 2.3.2 Training Tools, 4.2 Script Files, 8.1 Training Strategies, (, )
- HIPASS
- 5.4 Filterbank Analysis
- HK command
- 10.3 Tied Mixture Systems
- HLAB2NET(1.5)
- 2.4 Whats New in Version 2.0?
- HLABEL
- 2.1 HTK Software Architecture
- HLED
- 2.3.1 Data Preparation Tools, 3.1.4 Step 4 - Creating the Transcription Files, 3.3.1 Step 9 - Making Triphones from Monophones, 6.4 Editing Label Files, 12.5 Generating Forced Alignments, (, )
- HLIST
- 2.3.1 Data Preparation Tools, 5.12 Viewing Speech with HLIST, (, )
- HLM
- 2.1 HTK Software Architecture, 11.4 Bigram Language Models
- HLSTATS
- 2.3.1 Data Preparation Tools, 11.4 Bigram Language Models, (, )
- HMATH
- 2.1 HTK Software Architecture, 4 The Operating Environment
- HMEM
- 2.1 HTK Software Architecture, 4 The Operating Environment, 4.7 Memory Management
- HMM
- binary storage
- 3.3.1 Step 9 - Making Triphones from Monophones
- build philosphy
- 2.3.2 Training Tools
- cloning
- 3.3.1 Step 9 - Making Triphones from Monophones
- definition files
- 3.2.1 Step 6 - Creating Flat Start Monophones
- definitions
- 1.2 Isolated Word Recognition, 7 HMM Definition Files
- definitions
- 1.2 Isolated Word Recognition, 7 HMM Definition Files
- editor
- 2.3.2 Training Tools
- instance of
- 1.6 Continuous Speech Recognition
- parameters
- 7.1 The HMM Parameters
- triphones
- 3.3 Creating Tied-State Triphones
- HMM definition
- stream weight
- 7.2 Basic HMM Definitions
- basic form
- 7.2 Basic HMM Definitions
- binary storage
- 7.8 Binary Storage Format
- covariance matrix
- 7.2 Basic HMM Definitions
- formal syntax
- 7.9 The HMM Definition Language
- global features
- 7.2 Basic HMM Definitions
- global options
- 7.9 The HMM Definition Language
- global options macro
- 7.2 Basic HMM Definitions
- macro types
- 7.3 Macro Definitions
- macros
- 7.3 Macro Definitions
- mean vector
- 7.2 Basic HMM Definitions
- mixture components
- 7.2 Basic HMM Definitions
- multiple data streams
- 7.2 Basic HMM Definitions
- stream weight
- 7.2 Basic HMM Definitions
- symbols in
- 7.2 Basic HMM Definitions
- tied-mixture
- 7.5 Tied-Mixture Systems
- transition matrix
- 7.2 Basic HMM Definitions
- HMM lists
- 6.4 Editing Label Files, 7.4 HMM Sets, 7.4 HMM Sets, 8.5 Embedded Training using HEREST
- HMM name
- 7.2 Basic HMM Definitions
- HMM refinement
- 9 HMM System Refinement
- HMM sets
- 7.4 HMM Sets
- types
- 7.4 HMM Sets
- HMM tying
- 7.4 HMM Sets
- HMODEL
- 2.1 HTK Software Architecture
- HNET
- 1.6 Continuous Speech Recognition, 2.1 HTK Software Architecture
- HParm
- 2.1 HTK Software Architecture
- SILENERGY
- 5.9 Direct Audio Input/Output
- SILGLCHCOUNT
- 5.9 Direct Audio Input/Output
- SILMARGIN
- 5.9 Direct Audio Input/Output
- SILSEQCOUNT
- 5.9 Direct Audio Input/Output
- SPCGLCHCOUNT
- 5.9 Direct Audio Input/Output
- SPCSEQCOUNT
- 5.9 Direct Audio Input/Output
- SPEECHTHRESH
- 5.9 Direct Audio Input/Output
- HPARSE
- 2.3.3 Recognition Tools, 3.1.1 Step 1 - the Task Grammar, 11.3 Building a Word Network with HPARSE, (, )
- HParse format
- 11.3 Building a Word Network with HPARSE
- compatibility mode
- 11.3 Building a Word Network with HPARSE
- in V1.5
- 11.3 Building a Word Network with HPARSE
- variables
- 11.3 Building a Word Network with HPARSE
- HQUANT
- 2.3.1 Data Preparation Tools, (, )
- HREC
- 1.6 Continuous Speech Recognition, 2.1 HTK Software Architecture, 12.2 Decoder Organisation, 12.2 Decoder Organisation
- HREST
- 1.4 Baum-Welch Re-Estimation, 2.3.2 Training Tools, 8.1 Training Strategies, (, )
- HRESULTS
- 2.3.4 Analysis Tool, 12.4 Evaluating Recognition Results, (, )
- HSGEN
- 2.3.3 Recognition Tools, 3.1.3 Step 3 - Recording the Data, 11.6 Testing a Word Network using HSGEN, (, )
- HSHELL
- 2.1 HTK Software Architecture, 4 The Operating Environment
- HSIGP
- 2.1 HTK Software Architecture
- HSKind
- 7.4 HMM Sets
- HSLAB
- 2.3.1 Data Preparation Tools, 3.1.3 Step 3 - Recording the Data, (, )
- HSMOOTH
- 2.3.2 Training Tools, 10.4 Parameter Smoothing, (, )
- HSPIO(1.5)
- 2.4 Whats New in Version 2.0?
- HTKResearch(V1.5)
- 2.4 Whats New in Version 2.0?
- HTRAIN
- 2.1 HTK Software Architecture
- HUTIL
- 2.1 HTK Software Architecture
- HVITE
- 1.5 Recognition and Viterbi Decoding, 1.6 Continuous Speech Recognition, 2.3.3 Recognition Tools, 3.2.3 Step 8 - Realigning the Training Data, 3.4.1 Step 11 - Recognising the Test Data, 12.2 Decoder Organisation, (, )
- HVQ
- 2.1 HTK Software Architecture
- HWAVE
- 2.1 HTK Software Architecture
- HWAVEFILTER
- 5.8.4 NIST File Format
- insertion errors
- 12.3 Recognition using Test Databases
- integer values
- 4.3 Configuration Files
- <InvCovar>
- 7.2 Basic HMM Definitions
- isolated word training
- 8.1 Training Strategies
- item lists
- 3.3.1 Step 9 - Making Triphones from Monophones, 9.3 Parameter Tying and Item Lists
- indexing
- 9.3 Parameter Tying and Item Lists
- pattern matching
- 9.3 Parameter Tying and Item Lists
- JO command
- 10.3 Tied Mixture Systems
- K-means clustering
- 8.2 Initialisation using HINIT
- label files
- 6 Transcriptions and Label Files
- ESPS format
- 6.2.2 ESPS Label Files
- HTK format
- 6.2.1 HTK Label Files
- SCRIBE format
- 6.2.4 SCRIBE Label Files
- TIMIT format
- 6.2.3 TIMIT Label Files
- labels
- changing
- 6.4 Editing Label Files
- context dependent
- 6.4 Editing Label Files
- context markers
- 6.2.1 HTK Label Files
- deleting
- 6.4 Editing Label Files
- editing
- 6.4 Editing Label Files
- external formats
- 6.2 Label File Formats
- merging
- 6.4 Editing Label Files
- moving level
- 6.4 Editing Label Files
- multiple level
- 6.1 Label File Structure
- replacing
- 6.4 Editing Label Files
- side-by-side
- 6.1 Label File Structure
- sorting
- 6.4 Editing Label Files
- language model scaling
- 12.3 Recognition using Test Databases
- language models
- bigram
- 11.4 Bigram Language Models
- lattice
- comment lines
- 16.2 Format
- field names
- 16.2 Format
- format
- 2.3.3 Recognition Tools, 16.2 Format
- format
- 2.3.3 Recognition Tools, 16.2 Format
- header
- 16.1 SLF Files
- language model scale factor
- 12.7 N-Best Lists and Lattices
- link
- 16.1 SLF Files
- N-best
- 1.6 Continuous Speech Recognition
- node
- 16.1 SLF Files
- rescoring
- 1.6 Continuous Speech Recognition
- syntax
- 16.3 Syntax
- lattice generation
- 12.7 N-Best Lists and Lattices
- lattices
- 16.1 SLF Files
- library modules
- 2.1 HTK Software Architecture
- likelihood computation
- 1.2 Isolated Word Recognition
- linear prediction
- 5.3 Linear Prediction Analysis
- cepstra
- 5.3 Linear Prediction Analysis
- LINEIN
- 5.9 Direct Audio Input/Output
- LINEOUT
- 5.9 Direct Audio Input/Output
- live input
- 3.5 Running the Recogniser Live
- <LLTCovar>
- 7.2 Basic HMM Definitions
- log arithmetic
- 1.4 Baum-Welch Re-Estimation
- LOPASS
- 5.4 Filterbank Analysis
- LPC
- 5.3 Linear Prediction Analysis
- LPCEPSTRA
- 5.3 Linear Prediction Analysis
- LPCORDER
- 5.3 Linear Prediction Analysis
- LPREFC
- 5.3 Linear Prediction Analysis
- LS command
- 9.5 Tree-Based Clustering
- LT command
- 3.3.2 Step 10 - Making Tied-State Triphones, 3.5 Running the Recogniser Live, 9.5 Tree-Based Clustering
- M-heaps
- 4.7 Memory Management
- macro definition
- 7.3 Macro Definitions
- macro substitution
- 7.3 Macro Definitions
- macros
- 3.2.2 Step 7 - Fixing the Silence Models, 7.3 Macro Definitions
- special meanings
- 7.3 Macro Definitions
- types
- 7.3 Macro Definitions
- marking word boundaries
- 9.2 Constructing Context-Dependent Models
- master label files
- 3.1.4 Step 4 - Creating the Transcription Files, 6 Transcriptions and Label Files, 6.3.1 General Principles of MLFs, 8.2 Initialisation using HINIT, 8.5 Embedded Training using HEREST
- embedded label definitions
- 6.3.1 General Principles of MLFs
- examples
- 6.3.4 MLF Examples
- multiple search paths
- 6.3.1 General Principles of MLFs
- pattern matching
- 6.3.3 MLF Search
- patterns
- 3.1.4 Step 4 - Creating the Transcription Files, 6.3.3 MLF Search
- patterns
- 3.1.4 Step 4 - Creating the Transcription Files, 6.3.3 MLF Search
- search
- 6.3.3 MLF Search
- sub-directory search
- 6.3.3 MLF Search
- syntax
- 6.3.2 Syntax and Semantics
- wildcards
- 6.3.2 Syntax and Semantics
- master macro file
- 7.4 HMM Sets
- master macro files
- 3.2.1 Step 6 - Creating Flat Start Monophones
- input/output
- 9.1 Using HHED
- matrix dimensions
- 7.2 Basic HMM Definitions
- MAXCLUSTITER
- 10.2 Using Discrete Models with Speech
- maximum model limit
- 12.3 Recognition using Test Databases
- MAXTRYOPEN
- 4.8 Input/Output via Pipes and Networks
- ME command
- 6.4 Editing Label Files
- <Mean>
- 7.2 Basic HMM Definitions
- mean vector
- 7.1 The HMM Parameters
- MEASURESIL
- 12.6 Recognition using Direct Audio Input
- mel scale
- 5.4 Filterbank Analysis
- MELSPEC
- 5.4 Filterbank Analysis
- memory
- allocators
- 4.7 Memory Management
- element sizes
- 4.7 Memory Management
- statistics
- 4.7 Memory Management
- memory management
- 4.7 Memory Management
- MFCC coefficients
- 3.1.5 Step 5 - Coding the Data, 7.2 Basic HMM Definitions
- MICIN
- 5.9 Direct Audio Input/Output
- minimum occupancy
- 3.3.2 Step 10 - Making Tied-State Triphones
- MINMIX
- 9.6 Mixture Incrementing, 10.3 Tied Mixture Systems, 10.4 Parameter Smoothing
- <Mixture>
- 7.2 Basic HMM Definitions, 7.9 The HMM Definition Language
- mixture component
- 7.1 The HMM Parameters
- mixture incrementing
- 9.6 Mixture Incrementing
- mixture splitting
- 9.6 Mixture Incrementing
- mixture tying
- 10.3 Tied Mixture Systems
- mixture weight floor
- 9.6 Mixture Incrementing
- ML command
- 6.4 Editing Label Files
- MLF
- 3.1.4 Step 4 - Creating the Transcription Files, 6 Transcriptions and Label Files
- MMF
- 3.2.1 Step 6 - Creating Flat Start Monophones, 7.4 HMM Sets
- model compaction
- 3.3.2 Step 10 - Making Tied-State Triphones
- model training
- clustering
- 9.4 Data-Driven Clustering
- compacting
- 9.4 Data-Driven Clustering
- context dependency
- 9.2 Constructing Context-Dependent Models
- embedded
- 8.5 Embedded Training using HEREST
- embedded subword formulae
- 8.7.4 Embedded Model Reestimation(HEREST)
- forward/backward formulae
- 8.7.2 Forward/Backward Probabilities
- HMM editing
- 9.1 Using HHED
- in parallel
- 8.5 Embedded Training using HEREST
- initialisation
- 8.2 Initialisation using HINIT
- isolated unit formulae
- 8.7.3 Single Model Reestimation(HREST)
- mixture components
- 8.2 Initialisation using HINIT
- pruning
- 8.5 Embedded Training using HEREST
- re-estimation formulae
- 8.7 Parameter Re-Estimation Formulae
- sub-word initialisation
- 8.2 Initialisation using HINIT
- tying
- 9.3 Parameter Tying and Item Lists
- update control
- 8.2 Initialisation using HINIT
- Viterbi formulae
- 8.7.1 Viterbi Training (HINIT)
- whole word
- 8.2 Initialisation using HINIT
- monitoring convergence
- 8.2 Initialisation using HINIT, 8.5 Embedded Training using HEREST
- monophone HMM
- construction of
- 3.2 Creating Monophone HMMs
- MP command
- 11.7 Constructing a Dictionary
- MT command
- 9.7 Miscellaneous Operations
- MU command
- 9.6 Mixture Incrementing
- mu law encoded files
- 5.8.4 NIST File Format
- multiple alternative transcriptions
- 12.7 N-Best Lists and Lattices
- multiple hypotheses
- 16.1 SLF Files
- multiple recognisers
- 12.2 Decoder Organisation
- multiple streams
- 5.10 Multiple Input Streams
- rules for
- 5.10 Multiple Input Streams
- multiple-tokens
- 1.6 Continuous Speech Recognition
- N-best
- 1.6 Continuous Speech Recognition, 12.7 N-Best Lists and Lattices
- N-grams
- 11.4 Bigram Language Models
- NATURALREADORDER
- 4.9 Byte-swapping of HTK data files
- NATURALWRITEORDER
- 4.9 Byte-swapping of HTK data files
- NC command
- 9.4 Data-Driven Clustering
- network type
- 11.8 Word Network Expansion
- networks
- 11 NetworksDictionaries and Language Models
- in recognition
- 11.1 How Networks are Used
- word-internal
- 3.4.1 Step 11 - Recognising the Test Data
- new features
- in Version 2.0
- 2.4 Whats New in Version 2.0?
- in Version 2.1
- 2.4.1 Whats New in Version 2.1?
- NIST
- 2.3.4 Analysis Tool
- NIST format
- 12.4 Evaluating Recognition Results
- NIST scoring software
- 12.4 Evaluating Recognition Results
- NIST Sphere data format
- 5.8.4 NIST File Format
- non-emitting states
- 1.6 Continuous Speech Recognition
- non-printing chars
- 4.6 Strings and Names
- NSAMPLES
- 5.8.11 ALIEN and NOHEAD File Formats
- NUMCEPS
- 5.3 Linear Prediction Analysis, 5.4 Filterbank Analysis
- NUMCHANS
- 4.3 Configuration Files, 5.4 Filterbank Analysis
- <NumMixes>
- 7.2 Basic HMM Definitions, 7.6 Discrete Probability HMMs
- <NumStates>
- 7.9 The HMM Definition Language
- observations
- displaying structure of
- 5.12 Viewing Speech with HLIST
- operating system
- 4 The Operating Environment
- outlier threshold
- 3.3.2 Step 10 - Making Tied-State Triphones
- output filter
- 4.8 Input/Output via Pipes and Networks
- output lattice format
- 12.7 N-Best Lists and Lattices
- output probability
- continuous case
- 1.3 Output Probability Specification, 7.1 The HMM Parameters
- continuous case
- 1.3 Output Probability Specification, 7.1 The HMM Parameters
- discrete case
- 7.1 The HMM Parameters
- OUTSILWARN
- 12.6 Recognition using Direct Audio Input
- over-short training segments
- 8.4 Isolated Unit Re-Estimation using HREST
- parameter estimation
- 8 HMM Parameter Estimation
- parameter kind
- 5.7.1 HTK Format Parameter Files
- parameter tie points
- 7.3 Macro Definitions
- parameter tying
- 3.3.1 Step 9 - Making Triphones from Monophones
- parameterisation
- 3.1.5 Step 5 - Coding the Data
- partial results
- 12.3 Recognition using Test Databases
- path
- 1.5 Recognition and Viterbi Decoding, 1.5 Recognition and Viterbi Decoding
- as a token
- 1.6 Continuous Speech Recognition
- phone alignment
- 3.2.3 Step 8 - Realigning the Training Data
- phone mapping
- 3.2.3 Step 8 - Realigning the Training Data
- phone model initialisation
- 8.1 Training Strategies
- phone recognition
- 11.9 Other Kinds of Recognition System
- phones
- 8.1 Training Strategies
- PHONESOUT
- 5.9 Direct Audio Input/Output
- phonetic questions
- 9.5 Tree-Based Clustering
- pipes
- 4 The Operating Environment, 4.8 Input/Output via Pipes and Networks
- PLAINHS
- 7.4 HMM Sets
- pre-emphasis
- 5.2 Speech Signal Processing
- PREEMCOEF
- 5.2 Speech Signal Processing
- prompt script
- generationof
- 3.1.3 Step 3 - Recording the Data
- prototype definition
- 2.3.2 Training Tools
- pruning
- 2.3.2 Training Tools, 3.2.1 Step 6 - Creating Flat Start Monophones, 12.1 Decoder Operation, 12.3 Recognition using Test Databases
- in tied mixtures
- 10.3 Tied Mixture Systems
- pruning errors
- 8.5 Embedded Training using HEREST
- QS command
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.5 Tree-Based Clustering
- qualifiers
- 5.1 General Mechanism, 5.7.1 HTK Format Parameter Files
- _A
- 5.6 Delta and Acceleration Coefficients
- _C
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _C
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _C
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _D
- 5.6 Delta and Acceleration Coefficients
- _E
- 5.5 Energy Measures
- _K
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _K
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _K
- 5.7.1 HTK Format Parameter Files, 5.7.1 HTK Format Parameter Files, 5.15 Summary
- _N
- 5.6 Delta and Acceleration Coefficients, 5.10 Multiple Input Streams
- _N
- 5.6 Delta and Acceleration Coefficients, 5.10 Multiple Input Streams
- _O
- 5.5 Energy Measures
- _V
- 5.11 Vector Quantisation, 5.13 Copying and Coding using HCOPY, 10.2 Using Discrete Models with Speech
- _V
- 5.11 Vector Quantisation, 5.13 Copying and Coding using HCOPY, 10.2 Using Discrete Models with Speech
- _V
- 5.11 Vector Quantisation, 5.13 Copying and Coding using HCOPY, 10.2 Using Discrete Models with Speech
- _Z
- 5.4 Filterbank Analysis
- codes
- 5.7.1 HTK Format Parameter Files
- ESIG field specifiers
- 5.7.2 Esignal Format Parameter Files
- summary
- 5.15 Summary
- RAWENERGY
- 5.5 Energy Measures
- RE command
- 6.4 Editing Label Files
- realignment
- 3.2.3 Step 8 - Realigning the Training Data
- recogniser evaluation
- 3.4 Recogniser Evaluation
- recogniser performance
- 12.4 Evaluating Recognition Results
- recognition
- direct audio input
- 3.5 Running the Recogniser Live
- errors
- 12.4 Evaluating Recognition Results
- hypothesis
- 12.1 Decoder Operation
- network
- 12.1 Decoder Operation
- output
- 3.5 Running the Recogniser Live
- overall process
- 11.1 How Networks are Used
- results analysis
- 3.4.1 Step 11 - Recognising the Test Data
- statistics
- 12.4 Evaluating Recognition Results
- tools
- 2.3.3 Recognition Tools
- recording speech
- 3.1.3 Step 3 - Recording the Data
- RECOUTPREFIX
- 12.6 Recognition using Direct Audio Input
- RECOUTSUFFIX
- 12.6 Recognition using Direct Audio Input
- reflection coefficients
- 5.3 Linear Prediction Analysis
- regression formula
- 5.6 Delta and Acceleration Coefficients
- removing outliers
- 9.4 Data-Driven Clustering
- results analysis
- 2.3.4 Analysis Tool
- RO command
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.1 Using HHED, 9.4 Data-Driven Clustering
- RP command
- 11.7 Constructing a Dictionary
- RT command
- 9.7 Miscellaneous Operations
- SAVEASVQ
- 5.11 Vector Quantisation, 10.2 Using Discrete Models with Speech
- SAVEBINARY
- 7.8 Binary Storage Format, 9.2 Constructing Context-Dependent Models
- SAVECOMPRESSED
- 5.13 Copying and Coding using HCOPY
- SAVEWITHCRC
- 5.13 Copying and Coding using HCOPY
- script files
- 4.2 Script Files, 8.2 Initialisation using HINIT
- search errors
- 12.1 Decoder Operation
- segmental k-means
- 2.3.2 Training Tools
- sentence generation
- 11.6 Testing a Word Network using HSGEN
- SH command
- 9.4 Data-Driven Clustering
- SHAREDHS
- 7.4 HMM Sets
- short pause
- 3.2.2 Step 7 - Fixing the Silence Models
- signals
- for recording control
- 12.6 Recognition using Direct Audio Input
- silence floor
- 5.5 Energy Measures
- silence model
- 3.2.2 Step 7 - Fixing the Silence Models, 3.2.3 Step 8 - Realigning the Training Data, 9.3 Parameter Tying and Item Lists
- SILFLOOR
- 5.5 Energy Measures
- simple differences
- 5.6 Delta and Acceleration Coefficients
- SIMPLEDIFFS
- 5.6 Delta and Acceleration Coefficients
- single-pass retraining
- 8.6 Single-Pass Retraining
- singleton clusters
- 9.4 Data-Driven Clustering
- SK command
- 9.7 Miscellaneous Operations
- SLF
- 2.3.3 Recognition Tools, 3.1.1 Step 1 - the Task Grammar, 11 NetworksDictionaries and Language Models, 11.2 Word Networks and Standard Lattice Format
- arc probabilities
- 11.2 Word Networks and Standard Lattice Format
- format
- 11.2 Word Networks and Standard Lattice Format
- null nodes
- 11.2 Word Networks and Standard Lattice Format
- word network
- 11.2 Word Networks and Standard Lattice Format
- SO command
- 6.4 Editing Label Files
- software architecture
- 2.1 HTK Software Architecture
- SOURCEFORMAT
- 5.7.2 Esignal Format Parameter Files, 5.8 Waveform File Formats
- SOURCEKIND
- 5.1 General Mechanism, 12.6 Recognition using Direct Audio Input
- SOURCELABEL
- 6.2 Label File Formats, 6.4 Editing Label Files
- SOURCERATE
- 5.2 Speech Signal Processing, 5.9 Direct Audio Input/Output
- SP command
- 11.7 Constructing a Dictionary
- speaker identifier
- 12.4 Evaluating Recognition Results
- SPEAKEROUT
- 5.9 Direct Audio Input/Output
- speech input
- 5 Speech Input/Output
- automatic conversion
- 5.1 General Mechanism
- bandpass filtering
- 5.4 Filterbank Analysis
- blocking
- 5.2 Speech Signal Processing
- DC offset
- 5.2 Speech Signal Processing
- direct audio
- 5.9 Direct Audio Input/Output
- dynamic coefficents
- 5.6 Delta and Acceleration Coefficients
- energy measures
- 5.5 Energy Measures
- filter bank
- 5.4 Filterbank Analysis
- general mechanism
- 5.1 General Mechanism
- Hamming window function
- 5.2 Speech Signal Processing
- monitoring
- 5.12 Viewing Speech with HLIST
- pre-emphasis
- 5.2 Speech Signal Processing
- pre-processing
- 5.2 Speech Signal Processing
- summary of variables
- 5.15 Summary
- target kind
- 5.1 General Mechanism
- speech/silence detector
- 12.6 Recognition using Direct Audio Input
- speech;tex2html_html_special_mark_quot;input
- byte order
- 5.2 Speech Signal Processing
- SS command
- 9.7 Miscellaneous Operations, 10.3 Tied Mixture Systems
- ST command
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.5 Tree-Based Clustering
- stacks
- 4.7 Memory Management
- standard lattice format
- 2.3.3 Recognition Tools, 3.1.1 Step 1 - the Task Grammar, 11 NetworksDictionaries and Language Models, 11.2 Word Networks and Standard Lattice Format
- definition;tex2html_html_special_mark_quot;
- 16 HTK Standard Lattice Format (SLF)
- standard options
- 4.4 Standard Options
- -A
- 4.4 Standard Options
- -C
- 4.3 Configuration Files, 4.4 Standard Options
- -C
- 4.3 Configuration Files, 4.4 Standard Options
- -D
- 4.4 Standard Options
- -F
- 5.7.2 Esignal Format Parameter Files, 5.8 Waveform File Formats, 6.2 Label File Formats
- -F
- 5.7.2 Esignal Format Parameter Files, 5.8 Waveform File Formats, 6.2 Label File Formats
- -F
- 5.7.2 Esignal Format Parameter Files, 5.8 Waveform File Formats, 6.2 Label File Formats
- -G
- 6.2 Label File Formats
- -I
- 6.3.1 General Principles of MLFs
- -L
- 6.3.1 General Principles of MLFs, 6.4 Editing Label Files, 8.2 Initialisation using HINIT
- -L
- 6.3.1 General Principles of MLFs, 6.4 Editing Label Files, 8.2 Initialisation using HINIT
- -L
- 6.3.1 General Principles of MLFs, 6.4 Editing Label Files, 8.2 Initialisation using HINIT
- -S
- 4.2 Script Files, 4.4 Standard Options, 6.3.1 General Principles of MLFs
- -S
- 4.2 Script Files, 4.4 Standard Options, 6.3.1 General Principles of MLFs
- -S
- 4.2 Script Files, 4.4 Standard Options, 6.3.1 General Principles of MLFs
- -T
- 4.4 Standard Options, 8.2 Initialisation using HINIT
- -T
- 4.4 Standard Options, 8.2 Initialisation using HINIT
- -V
- 4.4 Standard Options
- summary
- 4.10 Summary
- state clustering
- 3.3.2 Step 10 - Making Tied-State Triphones
- state transitions
- adding/removing
- 9.7 Miscellaneous Operations
- state tying
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.4 Data-Driven Clustering
- statistics
- state occupation
- 3.3.1 Step 9 - Making Triphones from Monophones
- statistics file
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.1 Using HHED
- statistics;tex2html_html_special_mark_quot;file
- 9.4 Data-Driven Clustering
- <Stream>
- 7.2 Basic HMM Definitions, 7.5 Tied-Mixture Systems
- stream weight
- 1.3 Output Probability Specification, 7.1 The HMM Parameters
- <StreamInfo>
- 7.2 Basic HMM Definitions, 7.9 The HMM Definition Language
- streams
- 1.3 Output Probability Specification
- stress marking
- 3.1.2 Step 2 - the Dictionary
- string matching
- 12.4 Evaluating Recognition Results
- string values
- 4.3 Configuration Files
- strings
- metacharacters in
- 4.6 Strings and Names
- output of
- 4.6 Strings and Names
- rules for
- 4.6 Strings and Names
- SU command
- 9.7 Miscellaneous Operations
- sub-lattices
- 11.5 Building a Word Network with HBUILD, 16.1 SLF Files
- SUBLAT
- 11.5 Building a Word Network with HBUILD, 16.1 SLF Files
- SW command
- 9.7 Miscellaneous Operations
- <SWeights>
- 7.2 Basic HMM Definitions
- TARGETKIND
- 4.3 Configuration Files, 5.1 General Mechanism, 10.2 Using Discrete Models with Speech
- TARGETRATE
- 5.2 Speech Signal Processing
- task grammar
- 3.1.1 Step 1 - the Task Grammar, 11.3 Building a Word Network with HPARSE
- TB command
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.5 Tree-Based Clustering
- TC command
- 6.4 Editing Label Files, 9.2 Constructing Context-Dependent Models, 9.4 Data-Driven Clustering
- tee-models
- 3.2.2 Step 7 - Fixing the Silence Models, 7.7 Tee Models
- in networks
- 11.3 Building a Word Network with HPARSE
- termination
- 4.5 Error Reporting
- TI command
- 3.2.2 Step 7 - Fixing the Silence Models, 9.3 Parameter Tying and Item Lists
- tied parameters
- 9.3 Parameter Tying and Item Lists
- tied-mixture system
- 7.5 Tied-Mixture Systems
- tied-mixtures
- 9.3 Parameter Tying and Item Lists, 10.3 Tied Mixture Systems
- build procedure
- 10.3 Tied Mixture Systems
- output distribution
- 7.5 Tied-Mixture Systems
- tied-state
- 7.4 HMM Sets
- TIMIT database
- 3.1.2 Step 2 - the Dictionary, 6.4 Editing Label Files
- token history
- 12.1 Decoder Operation
- token passing
- 12.1 Decoder Operation
- Token Passing Model
- 1.6 Continuous Speech Recognition
- total likelihood
- 1.4 Baum-Welch Re-Estimation, 1.5 Recognition and Viterbi Decoding
- TR command
- 3.3.2 Step 10 - Making Tied-State Triphones
- tracing
- 4.4 Standard Options
- training
- sub-word
- 8.1 Training Strategies
- whole-word
- 8.1 Training Strategies
- training tools
- 2.3.2 Training Tools
- TRANSALT
- 6.1 Label File Structure, 6.4 Editing Label Files
- transcription
- orthographic
- 3.1.4 Step 4 - Creating the Transcription Files
- transcriptions
- model level
- 12.5 Generating Forced Alignments
- phone level
- 12.5 Generating Forced Alignments
- word level
- 12.5 Generating Forced Alignments
- transitions
- adding them
- 3.2.2 Step 7 - Fixing the Silence Models
- TRANSLEV
- 6.1 Label File Structure, 6.4 Editing Label Files
- <TransP>
- 7.2 Basic HMM Definitions
- tree building
- 3.3.2 Step 10 - Making Tied-State Triphones
- tree optimisation
- 9.5 Tree-Based Clustering
- triphones
- by cloning
- 3.3.1 Step 9 - Making Triphones from Monophones
- from monophones
- 3.3.1 Step 9 - Making Triphones from Monophones
- notation
- 3.3.1 Step 9 - Making Triphones from Monophones
- synthesising unseen
- 3.5 Running the Recogniser Live
- word internal
- 3.3.1 Step 9 - Making Triphones from Monophones
- tying
- examples of
- 9.3 Parameter Tying and Item Lists
- exemplar selection
- 9.3 Parameter Tying and Item Lists
- states
- 3.3.2 Step 10 - Making Tied-State Triphones
- transition;tex2html_html_special_mark_quot;matrices
- 3.3.1 Step 9 - Making Triphones from Monophones
- UF command
- 9.1 Using HHED
- under-training
- 10.4 Parameter Smoothing
- uniform segmentation
- 8.2 Initialisation using HINIT
- unseen triphones
- 3.3.2 Step 10 - Making Tied-State Triphones, 9.5 Tree-Based Clustering
- synthesising
- 9.5 Tree-Based Clustering
- up-mixing
- 9.6 Mixture Incrementing
- upper triangular form
- 7.2 Basic HMM Definitions
- <Use> clause (V1.5)
- 2.4 Whats New in Version 2.0?
- USEHAMMING
- 5.2 Speech Signal Processing
- USEPOWER
- 5.4 Filterbank Analysis
- USESILDET
- 5.9 Direct Audio Input/Output, 12.6 Recognition using Direct Audio Input
- UT command
- 9.3 Parameter Tying and Item Lists
- V1.5 grammar files
- 2.4 Whats New in Version 2.0?
- V1COMPAT
- 2.4 Whats New in Version 2.0?, 5.6 Delta and Acceleration Coefficients, 11.3 Building a Word Network with HPARSE
- varFloorN
- 8.2 Initialisation using HINIT
- <Variance>
- 7.2 Basic HMM Definitions
- flooring problems
- 3.3.2 Step 10 - Making Tied-State Triphones
- variance floor macros
- 3.2.1 Step 6 - Creating Flat Start Monophones
- generating
- 8.3 Flat Starting with HCOMPV
- variance floors
- 8.2 Initialisation using HINIT
- <VecSize>
- 7.2 Basic HMM Definitions, 7.9 The HMM Definition Language
- vector dimensions
- 7.2 Basic HMM Definitions
- vector quantisation
- 5.11 Vector Quantisation
- code book external format
- 5.11 Vector Quantisation
- distance metrics
- 5.11 Vector Quantisation
- type of
- 5.11 Vector Quantisation
- uses;tex2html_html_special_mark_quot;of
- 5.11 Vector Quantisation
- Version 1.5
- 2.4 Whats New in Version 2.0?
- Viterbi training
- 1.4 Baum-Welch Re-Estimation, 8.2 Initialisation using HINIT
- VQ codebook
- 2.4 Whats New in Version 2.0?
- VQTABLE
- 5.11 Vector Quantisation
- warning codes
- full listing
- 15 Error and Warning Codes
- warning message
- format
- 15 Error and Warning Codes
- warnings
- 4.5 Error Reporting
- waveform capture
- 12.6 Recognition using Direct Audio Input
- WB command
- 3.3.1 Step 9 - Making Triphones from Monophones, 9.2 Constructing Context-Dependent Models
- whole word modelling
- 8.1 Training Strategies
- whole word recognition
- 11.9 Other Kinds of Recognition System
- WINDOWSIZE
- 4.3 Configuration Files, 5.2 Speech Signal Processing
- WLR
- 1.6 Continuous Speech Recognition
- word equivalence
- 12.4 Evaluating Recognition Results
- word insertion penalty
- 3.4.1 Step 11 - Recognising the Test Data
- word internal
- 3.3.1 Step 9 - Making Triphones from Monophones
- Word Link Record
- 1.6 Continuous Speech Recognition
- word list
- 3.1.2 Step 2 - the Dictionary
- word N-best
- 1.6 Continuous Speech Recognition
- expansion rules
- 11.8 Word Network Expansion
- word networks
- tee-models in
- 11.3 Building a Word Network with HPARSE
- word spotting
- 11.9 Other Kinds of Recognition System
- word-end nodes
- 1.6 Continuous Speech Recognition, 1.6 Continuous Speech Recognition, 12.1 Decoder Operation
- word-internal network expansion
- 11.8 Word Network Expansion
- word-loop network
- 11.5 Building a Word Network with HBUILD
- word-pair grammar
- 11.5 Building a Word Network with HBUILD
- ZMEANSOURCE
- 5.2 Speech Signal Processing
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