IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007 · 11 citations · 5 references
EngineeringNeurolinguisticsSpoken Language ProcessingRecurrent Neural NetworkSpeech RecognitionNatural Language ProcessingPhoneticsComputational LinguisticsMemoryRobust Speech RecognitionVoice RecognitionLanguage StudiesLinguisticsCommand SentenceSpoken Command SentencesComputer ScienceSpeech CommunicationSpeech TechnologySpeech ProcessingSpeech InputSpeech PerceptionHidden Markov ModelsSpeech Interface
We have implemented a system that can understand spoken command sentences like "Bot lift green apple" using hidden Markov models (HMMs) and neural associative memories. After speaking a command sentence into a microphone, the system processes it in three stages: As first step, the auditory input is transformed into a convenient subsymbolic representation (diphones or triphones) using HMMs. The second step retrieves a symbolic representation (words) from the subsymbolic representation using a network of neural associative memories. Finally, in step three a semantic representation is obtained using neural associative memories. Furthermore, the system can learn new object words during performance.
5
Non-Holographic Associative Memory
David Willshaw, O. Buneman, H. C. Longuet–Higgins · Nature · 1969 · 1K citations
Storage (Memory), Engineering, Associative Memory (Psychology) +7
G Palm · Biological Cybernetics · 1980 · 445 citations
Engineering, Memory System, Associative Memory (Psychology) +5