The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech.
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 recently proposed the use of coefficients extracted from the 2D discrete cosine transform (DCT) of log Mel filter bank energies to improve speaker recognition over the traditional Mel frequency cepstral coefficients (MFCC) with appended deltas and double deltas (MFCC/deltas).
In this paper we propose softSAD: the direct integration of speech posteriors into a speaker recognition system instead of using speech activity detection (SAD).
By Harish Arsikere, Elizabeth Shriberg, Umut Ozertem
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.