12 accepted papers
Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and Hu-BERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these represe…
Pre-trained model representations have demonstrated state-of-the-art performance in speech recognition, natural language processing, and other applications. Speech models, such as Bidirectional Encoder Representations from Transformers (BERT) and Hidden units BERT (HuBERT), have enabled generating l…
The ability to automatically detect stuttering events in speech could help speech pathologists track an individual’s fluency over time or help improve speech recognition systems for people with atypical speech patterns. Despite increasing interest in this area, existing public datasets are too small…
Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. While modern speech technologies rely heavily on speech recognition and natural language understanding for speech content understanding, the investigation of…
This paper explores the use of multi-view features and their discriminative transforms in a convolutional deep neural network (CNN) architecture for a continuous large vocabulary speech recognition task. Mel-filterbank energies and perceptually motivated forced damped oscillator coefficient (DOC) fe…
Unseen data can degrade performance of deep neural net (DNN) acoustic models. To cope with unseen data, adaptation techniques are deployed. For unlabeled unseen data, one must generate some hypothesis given an existing model, which is used as the label for model adaptation. However, assessing the go…
Articulatory information can effectively model variability in speech and can improve speech recognition performance under varying acoustic conditions. Learning speaker-independent articulatory models has always been challenging, as speaker-specific information in the articulatory and acoustic spaces…
Speech recognition in varying background conditions is a challenging problem. Acoustic condition mismatch between training and evaluation data can significantly reduce recognition performance. For mismatched conditions, data-adaptation techniques are typically found to be useful, as they expose the…
Speech activity detection (SAD) is an essential component of most speech processing tasks and greatly influences the performance of the systems. Noise and channel distortions remain a challenge for SAD systems. In this paper, we focus on a dataset of highly degraded signals, developed under the DARP…
Speech-based depression detection has gained importance in recent years, but most research has used relatively quiet conditions or examined a single corpus per study. Little is thus known about the robustness of speech cues in the wild. This study compares the effect of noise and reverberation on de…
Research on detecting depression from speech has advanced in recent years, but most work has focused on the analysis of one corpus at a time. Given that clinical corpora are typically small, it is important to explore approaches that generalize across corpora and that could ultimately be adapted to…
Computational methods for speech-based detection of depression are still relatively new, and have focused on either a standard set of features or on specific additional approaches. We systematically study the effects of feature type, machine learning approach, and speaking style (read versus spontan…