Features

• decoder for large vocabulary continuous speech recognition
  • word conditioned tree search (supporting across-word models)
  • optimized HMM emission probability calculation using SIMD instructions
  • refined acoustic pruning using language model lookahead
  • word lattice generation
• feature extraction
  • a flexible framework for data processing: Flow
  • MFCC features
  • PLP features
  • Gammatone features
  • voicedness feature
  • vocal tract length normalization (VTLN)
  • support for several feature dimension reduction methods (e.g. LDA, PCA)
  • easy implementation of new features as well as easy integration of external features using Flow networks
• acoustic modeling
  • Gaussian mixture distributions for HMM emission probabilities
  • phoneme in triphone context (or shorter context)
  • across-word context dependency of phonemes
  • allophone parameter tying using phonetic decision trees (classification and regression trees, CART)
  • globally pooled diagonal covariance matrix (other types of covariance modelling are possible, but not fully tested)
  • maximum likelihood training
  • discriminative training (minimum phone error (MPE) criterion)
  • linear algebra support using LAPACK, BLAS
• language modeling
  • support for language models in ARPA format
  • weighted grammars (weighted finite state automaton)
• neural networks (new in v0.6)
  • training of arbitrarily deep feed-forward networks
  • CUDA support for running on GPUs
  • OpenMP support for running on CPUs
  • variety of activation functions, training criteria and optimization algorithms
  • sequence discriminative training, e.g. MMI or MPE (new in v0.7)
  • integration in feature extraction pipeline ("Tandem approach")
  • integration in search and lattice processing pipeline ("Hybrid NN/HMM approach")
• speaker adaptation
  • Constrained MLLR (CMLLR, "feature space MLLR", fMLLR)
  • Unsupervised maximum likelihood linear regression mean adaptation (MLLR)
  • speaker / segment clustering using Bayesian Information Criterion (BIC) as stop criterion
• lattice processing
• n-best list generation
• confusion network generation and decoding
• lattice rescoring
• lattice based system combination
• input / output formats
  • nearly all input and output data is in easily process-able XML or plain text formats
  • converter tools for the generation of NIST file formats are included
  • HTK lattice format
  • converter tools for HTK models