8 accepted papers
Speech emotion retrieval is an important technique for large-scale and high-quality data collection. Conventional approach using ensemble of classification models might limit the retrieved emotion diversity and/or underperform in out-of-domain acoustic conditions. Natural language is diverse and agn…
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not b…
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transfer across different domains, training a model on large-scale visual datasets is time consuming. On several audio event c…
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information. This structure can be defined by ontology and augmented in the classifier as a form of domain knowledge. To capture such dependencies between the labels…
In recent years, machine learning models for automated speech scoring systems were mainly built using data-driven approaches with handcrafted features as one of the main components. However, the remarkable successes of deep learning (DL) technology in a variety of machine learning tasks has demonstr…
We investigate two deep learning architectures reported to have superior performance in ASR over the conventional GMM system, with respect to automatic speech scoring. We use an approximately 800-hour large-vocabulary non-native spontaneous English corpus to build three ASR systems. One system is in…
The lack of available large corpora of transcribed whispered speech is one of the major roadblocks for development of successful whisper recognition engines. Our recent study has introduced a Vector Taylor Series (VTS) approach to pseudo-whisper sample generation which requires availability of only…
Despite recent advancements in digital signal processing technology for cochlear implant (CI) devices, there still remains a significant gap between speech identification performance of CI users in reverberation compared to that in anechoic quiet conditions. Alternatively, automatic speech recogniti…