SLM

(redirected from Statistical Language Model)
AcronymDefinition
SLMSalem (Amtrak station code; Salem, OR)
SLMSpatial Light Modulator
SLMService Level Management (ATMF)
SLMSelective Laser Melting (manufacturing)
SLMSustainable Land Management
SLMStudent Loan Marketing (aka Sallie Mae; aka SLMA)
SLMSliema (postal locality, Malta)
SLMSprache Literatur Medien (German: Language Literature Media)
SLMSudan Liberation Movement
SLMSituational Leadership Model
SLMStraight Line Motion
SLMStraight Line Method (accounting)
SLMSoftware License Management (also seen as SWLM)
SLMSound Level Meter
SLMSri Lankan Military
SLMSecond Line Maintenance (technological assistance service)
SLMSingle License Manager
SLMService Level Monitoring
SLMService Level Management
SLMService Lifecycle Management
SLMStrict Low Middling (standard)
SLMSimulation Lifecycle Management
SLMSurinam Airways (ICAO code)
SLMStandard Linear Model (rock rheology)
SLMSingle Longitudinal Mode (SONET)
SLMSul Livello del Mare (Italian: Above Sea Level)
SLMStructured Language Model (various locations)
SLMStatistical Language Model
SLMSoftware License Monitoring (intellectual property)
SLMStudent Lifecycle Management (software)
SLMStandard Liters per Minute
SLMSenior Leadership Management (education; New Zealand)
SLMShared Lane Marking (bicycling; various locations)
SLMSport Leisure Management (UK)
SLMSelf Leveling Machine (various companies)
SLMSource of Light Ministries (Madison, GA)
SLMSurinaamse Luchtvaart Maatschappij (Surinam Airlines)
SLMSad Little Man
SLMSocial Local Mobile (also seen as SoLoMo)
SLMSpanish Language Media
SLMSoftware Lifecycle Management
SLMSpeech Learning Model
SLMSmall Library Management (Texas)
SLMSelective Level Meter
SLMSpeed Loop Motor (Control Techniques)
SLMSignal Label Mismatch (SONET)
SLMSingle-Level Metal
SLMStandard Laboratory Module
SLMShining Light Ministries (Mission, TX)
SLMService Layer Manager (Ciena)
SLMSignaling Link Management
SLMSystem Load Module
SLMStudent Life Ministry
SLMShoulder Launched Munitions (MILES Component)
SLMShip Logistic Manager
SLMSubscriber Loop Multiplex
SLMSenior Life Master (bridge)
SLMService Life Monitoring
SLMSeat Lift Mechanism (toilet component)
SLMShort Life Memorandum (UK)
SLMStop Like Man
SLMSea Level Measurement
SLMStructure Location Maps
SLMShear Load Measurement
SLMStaff Level Meeting
SLMShip/Submarine Logistics Manager
SLMSystem Library Manager
SLMSide Lobe Measurement
SLMSupply of Labor and Materials (Philippines)
SLMSystem List Model
SLMSynoptic Lagrangian Map
SLMSubmarine Launched Missile
SLMSilver Life-Saving Medal
SLMSynthetic Layer Microstructures
SLMSocially Limiting Maneuver
SLMSkip Level Meeting (Sprint)
SLMStrategic Logical Model (Sprint)
SLMShoulder Launched Missile
SLMSmart Little People (modeling)
SLMSaint Lawrence Martyr Church & School (Redondo Beach, CA)
SLMStatic Load Model
SLMSmart Learning Module
References in periodicals archive ?
Some ideas discussed so far include (1) languages for defining intelligent, conversational dialogues; (2) communication standards between different virtual assistants; (3) standards for statistical language models to support more flexible speech recognition; and (4) standard semantic representations for concepts common in virtual assistant applications, like time and location.
Spoken Dialog Design covered speech recognition, synthesis, dictionaries, grammars, statistical language models, NLP, discourse structure, user interface design, and error recovery.
Novauris contends that statistical language models don't work well for long utterances, and that slot-filling procedures are more powerful than previously thought.
* large statistical language models management, which improves support for natural language applications; and
Statistical language models (SLMs): a more sophisticated method in which language statistics, along with a statistical classifier, are used to place recognized utterances into one of several meaning categories.
The main requirements for a high quality ASR are: speaker independence, enabling the recognition of continuous speech from any speaker, without prior training; high accuracy, along with the capability to return a set of N-best hypotheses together with reliable confidence values, which is key to building a good dialogue-flow management; and the capability to support both grammar-based applications and the use of Statistical Language Models for more complex interactions.
He has also established research partnerships with several European universities to develop or improve commercial speech recognition applications, and was instrumental in crafting SpeechCycle's High Definition Statistical Language Models, which recognize specific caller issues.
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