Neural Dynamics of Phonetic Trading Relations for Variable-Rate CV Syllables

The perception of CV syllables exhibits a trading relationship between voice onset time (VOT) of a consonant and duration of a vowel. Percepts of [ba] and [wa] can, for example, depend on the durations of the consonant and vowel segments, with an increase in the duration of the subsequent vowel switching the percept of the preceding consonant from [w] to [b]. A neural model, called PHONET, is proposed to account for these findings. In the model, C and V inputs are filtered by parallel auditory streams that respond preferentially to transient and sustained properties of the acoustic signal, as in vision. These streams are represented by working memories that adjust their processing rates to cope with variable acoustic input rates. More rapid transient inputs can cause greater activation of the transient stream which, in turn, can automatically gain control the processing rate in the sustained stream. An invariant percept obtains when the relative activations of C and V representations in the two streams remain uncha.nged. The trading relation may be simulated as a result of how different experimental manipulations affect this ratio. It is suggested that the brain can use duration of a subsequent vowel to make the [b]/[w] distinction because the speech code is a resonant event that emerges between working mernory activation patterns and the nodes that categorize them.Advanced Research Projects Agency (90-0083); Air Force Office of Scientific Reseearch (F19620-92-J-0225); Pacific Sierra Research Corporation (91-6075-2

Self-organized learning in multi-layer networks

We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learnin

Audio Event Detection using Weakly Labeled Data

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.Comment: ACM Multimedia 201