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Index

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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