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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
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Features * Text-dependent algorithm prevents unauthorized access with a covertly-recorded user voice.
* Two-factor authentication by checking voice biometrics and pass-phrase authenticity.
* Regular microphones and smartphones are suitable for recording user voices.
* Available as a multiplatform SDK that supports multiple programming languages.
* Reasonable prices, flexible licensing and free customer support.
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LanguagesCPP |
LanguagesC CPP CS VB.NET |
Source TypeClosed
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Source TypeClosed
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License TypeProprietary |
License TypeProprietary |
OS Type |
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