Improving Adaptive Learning Models Using Prosodic Speech Features

Cognitive models of memory retrieval aim to describe human learning and forgetting over time. Such models have been successfully applied in digital systems that aid in memorizing information by adapting to the needs of individual learners. The memory models used in these systems typically measure the accuracy and latency of typed retrieval...

The representation of speech and its processing in the human brain and deep neural networks

For most languages in the world and for speech that deviates from the standard pronunciation, not enough (annotated) speech data is available to train an automatic speech recognition (ASR) system. Moreover, human intervention is needed to adapt an ASR system to a new language or type of speech. Human listeners, on the other hand, are able to...

The neural correlates underlying lexically-guided perceptual learning

There is ample evidence showing that listeners are able to quickly adapt their phoneme classes to ambiguous sounds using a process called lexically-guided perceptual learning. This paper presents the first attempt to examine the neural correlates underlying this process. Specifically, we compared the brain’s responses to ambiguous [f/s] sounds...

The Time-Course of Phoneme Category Adaptation in Deep Neural Networks

Both human listeners and machines need to adapt their sound categories whenever a new speaker is encountered. This perceptual learning is driven by lexical information. In previous work, we have shown that deep neural network-based (DNN) ASR systems can learn to adapt their phoneme category boundaries from a few labeled examples after exposure ...

Visualizing Phoneme Category Adaptation in Deep Neural Networks

Both human listeners and machines need to adapt their sound categories whenever a new speaker is encountered. This perceptual learning is driven by lexical information. The aim of this paper is two-fold: investigate whether a deep neural network-based (DNN) ASR system can adapt to only a few examples of ambiguous speech as humans have been found...

Building an ASR System for Mboshi Using A Cross-language Definition of Acoustic Units Approach

For many languages in the world, not enough (annotated) speech data is available to train an ASR system. Recently, we proposed a cross-language method for training an ASR system using linguistic knowledge and semi-supervised training. Here, we apply this approach to the low-resource language Mboshi. Using an ASR system trained on Dutch, Mboshi...