Speech to personal assistants (e.g., reminders, calendar entries, messaging, voice search) is often uttered under cognitive load, causing nonfinal pausing that can result in premature recognition cut-offs.
We describe the SRI BioFrustration Corpus, an inprogress corpus of time-aligned audio, video, and autonomic nervous system signals recorded while users interact with a dialog system to make returns of faulty consumer items.
We present a system for detection of lexical stress in English words spoken by English learners. This system was designed to be part of the EduSpeak® computer-assisted language learning (CALL) software.
While human listeners can understand speech in reverberant conditions, indicating that the auditory system is robust to such degradations, reverberation leads to high word error rates for automatic speech recognition (ASR) systems.
Recurrent Neural Networks (RNN) have been successfully applied for improved speech recognition and statistical machine translation (SMT) for N-best list re-ranking.
Though depression is a common mental health problem with significant impact on human society, it often goes undetected. We explore a diverse set of features based only on spoken audio to understand which features correlate with self-reported depression scores according to the Beck depression...
Deep Neural Network (DNN) based acoustic models have shown significant improvement over their Gaussian Mixture Model (GMM) counterparts in the last few years.
We recently proposed the use of deep neural networks (DNN) in place of Gaussian Mixture models (GMM) in the i-vector extraction process for speaker recognition.
We present improvements to a keyword spotting (KWS) system that operates in highly adverse channel conditions with very low signal-to-noise ratio levels.