Automatic Speaker Recognition: Recent Progress, Current Applications, and Future Trends

1

• Seminar
• Speech Recognition Projects
• E.M. Bakker
• LIACS Media Lab
• Leiden University

2
Project Outline

• Implementation
• Project Modules
• Speech Database
• Speech Signal Analysis
• Hidden Markov Models Training
• Language Models Training
• Recognition Algorithms
• Evaluation

3
Implementation

• A Safe C Programming Style
• Not to be used in C
• Syntax and Programming Style
• Conventions
• Basic Design Rules
• Program Services
• Memory Services
• Diagnostics
• Important Topics
• Portability
• Testing
• Reliability

4
Implementation A Safe C Programming Style

• Features to be avoided, or not to be used in C
• C inherited features
• if(c0), ?, , ,goto, break, continue, union,
  struct, bit-wise, ( !), int, short, double,
  unsigned, , --, explicit constant numbers,
  cast, variable argument lists
• Preprocessor features
• macros for constants, macros for functions,
  pragma, compiler/platform specific directives
• Object Oriented
• global data, global non-member functions, public
  data, friend, overloading operators_at_, ,...
• Memory and pointer-related
• pointers, new, delete, malloc, free(), pointers
  to functions, -gt, -gt ., const char, NULL, type
  ref - t, type count, type count, type
  count, type (count), type (count)
• printf, scanf, assembly language, object passed
  by val and temporary objects

5
Implementation Syntax and Programming Style

• Programs in plain English
• Meaningful names
• One statement per line
• const for data and methods whenever possible
• variables local whenever possible
• private/protected data members whenever possible
• do not use confusing syntax like
• if (a)
• for (I0Ilt4)
• always use default in switch-statement
• use assert in all the critical points

6
Implementation Conventions

• Functions and methods My_Example_Function()
• Variables my_example_var
• Classes MyExampleClass
• Constants MY_EXAMPLE_CONSTANT
• In general meaningful names, except for indices
• Comment
• file-description
• version history (bugs new functionality)
• user information (user guide)
• implementation information (reference guide)
• code comment

7
Implementation Basic Design Rules

• Project modularity achieved through classes.
• Structure the program by Classes only (only
  methods are allowed, no separate functions)
• Project is decomposed into modules with as little
  cross-dependence as possible
• One module per class
• Classes should have minimal interfaces
• Modules should have minimal dependencies
• Implementation issues hidden from clients
  (information hiding)
• Inheritance should be extensively used
• Advantages
• Improved readability
• Reduced maintenance work
• Improved robustness

8
Implementation Program Services

• Safe memory management
• memory service
• dynamic memory management C without pointers
• Diagnostics
• decide which data must be checked when, and
  define the actions
• File management, user interfaces
• User program configuration management
• Text data management
• Mathematical data management

9
Implementation Memory Services Diagnostics

• Memory Services
• Diagnostics

10
Some Important Topics

• Portability
• portability and defined options in files
  compatib.h, defopt.h, Boolean.h
• Testing
• test routines and version history
• Reliability
• readability
• maintainability

11
RES General Specification

• RES (Recognition Experimental System) is an HMM
  based experimental tool for continuous
  multispeaker speech recognition. The system works
  on recorded speech files and it basically
  includes
• the batch modules for acoustic model
  initialization and training
• grammar models training
• phoneme/word recognition
• performance evaluation.
• RES is state of art in speaker independent
  phonetic recognition
• with 69.2 of percent correct using all TIMIT
  test data using context independent phonetic
  models.
• It yields 87.83 of percent correct in speaker
  independent word recognition on ATIS using
  context independent phonetic models not optimally
  tuned on this database.

12
RES General Specification

• How to build an ASR system for a different
  language?
• we need many segmented speech recordings to feed
  the training programs and get good HMM models of
  our voices.
• use a freeware program like Snack 1.4 (search on
  the Internet) to prepare the data.
• search a Dutch multispeaker phonetic database.
• Design and feed the right language-model.
• Speech samples to train and test the RES system?
• You can download speech samples from Linguistic
  Data Consortium (LDC) after you have obtained a
  user account.

13
General Specification
14
General Specification

• Required C custom libraries
• none
• Portability
• Linux
• Windows 3.x, Windows 95, NT
• DOS with DjGpp
• Compilers
• Ms Visual C gt4.0
• DjGpp version 2.8.1 or
• GNU Linux Gpp version 2.8.1 or newer

15
Speech Database

• Speech data retrieval
• Speech files
• NIST1A (ATIS x, TIMIT),
• MS WAV
• custom, adding software drivers
• Label File
• ATIS
• IMIT
• various subsets, custom labels alphabets included
  in a file, custom label handling supplying a
  driver.
• Other options
• overlap
• window length
• file buffering

16
Speech Signal Analysis

• Feature Extraction
• Signal processing
• Any concatenation of processing blocks is
  allowed. Each block performs a class of
  processing and the actual processing is specified
  by the options.
• Available processing blocks
• Preemphasis_and_Hamming
• Mean_Subtraction
• FFT
• MFCC with Log/non Log Energy
• any order differences
• Other Blocks can be added supplying proper
  drivers.

17
Hidden Markov Models

• HMM model Initialization
• HMM topology
• 4 predefined types with configurable number of
  states.
• Acoustic Units
• as allowed by the available database
• emission densities
• Untied Gaussian mixtures
• full or diagonal covariance matrix
• number of mixtures configurable for each acoustic
  unit
• Initialization method
• maximum distortion splitting on segmented database

18
Hidden Markov Models Training

• Training algorithm
• Single and Simultaneous Model Re-estimation
  Baum-Welch.
• parameter re-estimation selective by
  configuration.

19
Language Models

• Language Model
• unigram and bigram on words and phonemes
• Smoothing techniques
• Good-Turing, non-linear and linear interpolation
  model
• Word Clustering minimum mean square error on
  transition probability
• Perplexity
• word and phoneme based computation

20
Recognition

• Recognition
• Recognition Unit
• acoustic units, words
• Algorithm Type
• Viterbi with Beam search and Window search
  pruning strategies

21
Evaluation

• Evaluation Wagner-Fisher algorithm

22
Projects

• 1. Dutch Speech Corpus Database Interface (2
  groups)
• in an early phase some example classes should be
  available, like counting, etc.
• maybe use tools like praat (for wav labeling
  with phonetics), etc.
• 2. Signal Analysis and Feature Extraction (2
  groups)
• 3. HMM Initialization HMM Dutch Phonetic
  Training (2 groups)
• 4. Dutch Language Model Word Class Training (2
  groups)
• in an early phase some examples should be
  available
• 5. Recognition (2 groups)
• Evaluation (All)

23
Project Designs

• The design of the project should contain the
  following
• The implementation goals
• The underlying technique and theory
• A functional description of the starting-code and
  tools
• The design of new code and functionality
• Implementation goals and a time-scheme
• NB if it is considered difficult to obtain all
  the goals within the current time-frame, team up
  with the other team
• Interfacing
• Define the module-interfaces
• Define the time-path for the essential
  module-inputs
• Define a realistic time-path for the
  (partial-)outputs of the module.