Author: Colleen Richey
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Speech‐based markers for post traumatic stress disorder in US veterans
This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls.
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Mapping Individual to Group Level Collaboration Indicators Using Speech Data
To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level.
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Robust Speaker Recognition from Distant Speech under Real Reverberant Environments Using Speaker Embeddings
This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment.
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Voices Obscured in Complex Environmental Settings (VOiCES) corpus
This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.
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The SRI speech-based collaborative learning corpus
We introduce the SRI speech-based collaborative learning corpus, a novel collection designed for the investigation and measurement of how students collaborate together in small groups.
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Privacy- preserving speech analytics for automatic assessment of student collaboration
This work investigates whether nonlexical information from speech can automatically predict the quality of small-group collaborations. Audio was collected from students as they collaborated in groups of three to solve math problems.
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Spoken Interaction Modeling for Automatic Assessment of Collaborative Learning
This study investigates whether automatic audio- based monitoring of interactions can predict collaboration quality.
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Classification of Lexical Stress Using Spectral and Prosodic Features for Computer-assisted Language Learning Systems
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.
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The SRI AVEC-2014 Evaluation System
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 rating scale.
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Emotion Detection in Speech Using Deep Networks
We propose a novel staged hybrid model for emotion detection in speech. Hybrid models exploit the strength of discriminative classifiers along with the representational power of generative models.
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Lexical Stress Classification for Language Learning Using Spectral and Segmental Features
We present a system for detecting lexical stress in English words spoken by English learners. The system uses both spectral and segmental features to detect three levels of stress for each syllable in a word.
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Articulatory trajectories for large-vocabulary speech recognition
We present a neural network model to estimate articulatory trajectories from speech signals where the model was trained using synthetic speech signals generated by Haskins Laboratories’ task-dynamic model of speech production.