Neural Associative Memories and Hidden Markov Models for Speech Recognition

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.