This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls.
Tackling Unseen Acoustic Conditions in Query-by-Example Search Using Time and Frequency Convolution for Multilingual Deep Bottleneck Features
This paper revisits two neural network architectures developed for noise and channel robust ASR, and applies them to building a state-of-art multilingual QbE system.
Joint modeling of articulatory and acoustic spaces for continuous speech recognition tasks
This paper investigates using deep neural networks (DNN) and convolutional neural networks (CNNs) for mapping speech data into its corresponding articulatory space.
Toward human-assisted lexical unit discovery without text resources
This work addresses lexical unit discovery for languages without (usable) written resources.
Speech recognition in unseen and noisy channel conditions
This work investigates robust features, feature-space maximum likelihood linear regression (fMLLR) transform, and deep convolutional nets to address the problem of unseen channel and noise conditions in speech recognition.
Unsupervised Learning of Acoustic Units Using Autoencoders and Kohonen Nets
This work investigates learning acoustic units in an unsupervised manner from real-world speech data by using a cascade of an autoencoder and a Kohonen net.
Fusion Strategies for Robust Speech Recognition and Keyword Spotting for Channel- and Noise-Degraded Speech
Current state-of-the-art automatic speech recognition systems are sensitive to changing acoustic conditions, which can cause significant performance degradation.
Improving robustness against reverberation for automatic speech recognition
In this work, we explore the role of robust acoustic features motivated by human speech perception studies, for building ASR systems robust to reverberation effects.
Speech-based assessment of PTSD in a military population using diverse feature classes
We analyzed recordings of the Clinician-Administered PTSD Scale (CAPS) interview from military personnel diagnosed as PTSD positive versus negative.