Julius
Julius is a high-performance, two-pass large vocabulary
continuous speech
recognition (LVCSR) engine.
It supports N-gram based dictation, DFA grammar based parsing,
and one-
pass isolated word recognition. Phone context dependencies are
supported
up to triphone. It can perform a multi-model decoding, a recognition
using
several LMs and AMs simultaneously with a single processor, and also
supports "hot plugging" of arbitrary modules at run time.
The acoustic models and language models
are pluggable, and you can build various types of speech recognition
system by building your own models and modules to be suitable for your
task. It also adopts standard formats to cope with other toolkit such
as HTK, CMU-Cam SLM toolkit, etc.
The core engine is implemented as embeddable library, to aim to offer
speech recognition capability to various applications. The recent
version supports plug-in capability so that the engine can be extended
by user.
Julius 4.2.2
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Price
Free to download
Size
1.7MB
License
Open source license
Developer
Lee Akinobu
Website
julius.sourceforge.jp
System Requirements
Support
Sites:
Documentation,
Forums
Selected
Reviews:
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Features include:
- Real-time, hi-speed, accurate recognition based on 2-pass
strategy
- Low
memory requirement: less than 32MBytes required for work area
(<64MBytes for 20k-word dictation with on-memory 3-gram LM)
- Supports LM of N-gram, grammar, and isolated word
- Language and unit-dependent: Any LM in ARPA standard format
and AM in
HTK ascii hmmdefs format can be used
- Highly
configurable: can set various search parameters. Also alternate
decoding algorithm (1-best/word-pair approx., word trellis/word graph
intermediates, etc.) can be chosen
- Full source code documentation and manual in Engligh /
Japanese
- On-the-fly recognition for microphone
and network input
- GMM-based input rejection
- Successive decoding, delimiting input by
short pauses
- N-best output
- Word graph output
- Forced alignment on word, phoneme, and
state level
- Confidence scoring
- Server mode and control API
- Many search parameters for tuning its
performance
- Character code conversion for result
output
- Long N-gram support
- Run with forward / backward
N-gram only
- Confusion network output
- Arbitrary multi-model decoding
in a single thread
- Rapid isolated word recognition
- User-defined LM function
embedding
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Last Updated Monday, March 25 2013 @ 04:16 PM EST
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