By John G. Webster (Editor)
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Additional info for 58.Speech and Audio Processing
The size of ;; ;; ;;; NEURAL NETS FOR SPEECH PROCESSING 327 Output neuron group Hidden (state) neuron groups z –1 Inputs Group of weights with information flow z –1 Delay line t–1 (a) (b) the hidden layer is in general between 64 and 4096 neurons, depending on the amount of available training data, which results in about 10,000 to 2 million weights. Time-delay neural networks (TDNNs) (13) have the same structure as a regular MLP, but they have a reduced number of total weight parameters and have proven to be a useful improvement over regular MLPs in many applications, where the amount of training data is low compared to the number of parameters to estimate.
XT͖ being the input vector feature sequence (frames) calculated from the observed waveform, in practical systems around 100 40-dimensional vectors/s input speech, with C ϭ cT1 ϭ ͕c1, c2, . , cT͖ being any valid symbol sequence and C* being the recognized symbol sequence with the highest probability among all possible sequences. In the case of word recognition, valid symbol classes ct are any words which are listed in a pronunciation dictionary that contains all words to be recognized as phoneme sequences; and in the case of phoneme recognition, valid symbol classes ct are all possible phonemes, which are, depending on the language to recognize, usually around 50.
XY or e-cube routing). If the deterministic routing scheme encounters a blocked node output port, the adaptive protocol will choose a different output port that will bring the message closer to its destination. This way, a message either reaches its destination or is blocked when no other output port is available that would bring the message closer to its destination. The resulting path will always be of minimal length, and the network performance will be increased over the deterministic routing scheme because a message is allowed to take alternative paths.
58.Speech and Audio Processing by John G. Webster (Editor)