Speech & natural language publications
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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.Ā
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Content Matching for Short Duration Speaker Recognition
We show how content matching can be effectively done at the statistics level to enable the use of standard veriļ¬cation backends. While no signiļ¬cant improvements were observed for the general…
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A Deep Neural Network Speaker Veriļ¬cation System Targeting Microphone Speech
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.Ā
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Application of Convolutional Neural Networks to Speaker Recognition in Noisy Conditions
This paper applies a convolutional neural network (CNN) trained for automatic speech recognition (ASR) to the task of speaker identification (SID).
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Evaluating Robust Features on Deep Neural Networks for Speech Recognition in Noisy and Channel Mismatched Conditions
In this work we present a study exploring both conventional DNNs and deep Convolutional Neural Networks (CNN) for noise- and channel-degraded speech recognition tasks using the Aurora4 dataset.Ā
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Recent Improvements in SRIās Keyword Detection System for Noisy Audio
We present improvements to a keyword spotting (KWS) system that operates in highly adverse channel conditions with very low signal-to-noise ratio levels.Ā
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Spoken Language Recognition Based on Senone Posteriors
This paper explores in depth a recently proposed approach to spoken language recognition based on the estimated posteriors for a set of senones representing the phonetic space of one or…
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Identifying User Demographic Traits through Virtual-World Language Use
The paper presents approaches for identifying real-world demographic attributes based on language use in the virtual world.
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Articulatory Features from Deep Neural Networks and Their Role in Speech Recognition
This paper presents a deep neural network (DNN) to extract articulatory information from the speech signal and explores different ways to use such information in a continuous speech recognition task.
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Application of Convolutional Neural Networks to Language Identification in Noisy Conditions
This paper proposes two novel frontends for robust language identification (LID) using a convolutional neural network (CNN) trained for automatic speech recognition (ASR).
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Trial-Based Calibration for Speaker Recognition in Unseen Conditions
This work presents Trial-Based Calibration (TBC), a novel, automated calibration technique robust to both unseen and widely varying conditions.
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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.Ā The system uses both spectral and segmental features to detect three levels of stress for…