Features and benefits
- Hidden Markov Model (HMM)-based speech recognizer – State of the art accuracy
- Continuous speech – No need for pauses, user speaks naturally
- Dynamic grammar compilation – Enables complex application workflows in a small
footprint speech recognizer - Speaker independent – No tedious user training session required
- Speaker adaptation – Automatically adjusts to different speakers and accents
- C++ implementation – Portable to a range of hardware/software configurations
- Noise filtering design tools – Rapid tuning for noisy acoustic environments without time-
consuming and expensive acoustic model development - Dynamic noise compensation – Realtime differentiation between background noise and
speaker - Floating point or integer versions – Wide choice of hardware options
- Supports finite state (command and control) or statistical (free form) grammars – More flexible, natural application designs
- Supports push-to-talk, hold-to-talk, and open mic recording – Multiple user interface
options - Distributed speech recognition over low-bandwidth networks – Low-cost, high-accuracy
deployment option for speech recognition on mobile devices
Technical specifications
CPU requirements
200 MHz StrongArm, 66 MHz Intel x86 (support for other processors on request)
Memory requirements
- Total: 750KB-2.250MB
- Executable (ROM): 350-750KB
- Acoustic models (ROM): 100-500KB
- Active search (RAM): 300KB-1MB (more for complex grammars)
Supported languages
- Adults: American and British English, Latin American Spanish, Iraqi Arabic, Pashto and
Dari (others on request) - Children: American English
Grammars
- Statistical or JSGF forms; static or dynamic; dictation option
Operating systems
- Windows, Mac OS X, Linux and Android (others on request)
Development environment
- C/C++, Java (via JNI), client/server versions available
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