Speech & natural language publications
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Joint modeling of articulatory and acoustic spaces for continuous speech recognition tasks
This paper investigates using deep neural networks (DNN) and convolutional neural networks (CNNs) for mapping speech data into its corresponding articulatory space.
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Speech recognition in unseen and noisy channel conditions
This work investigates robust features, feature-space maximum likelihood linear regression (fMLLR) transform, and deep convolutional nets to address the problem of unseen channel and noise conditions in speech recognition.
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Multi-microphone speech recognition integrating beamforming, robust feature extraction, and advanced DNN/RNN backend
This paper gives an in-depth presentation of the multi-microphone speech recognition system we submitted to the 3rd CHiME speech separation and recognition challenge and its extension.
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Conversational In-Vehicle Dialog Systems: The past, present, and future
We review research and development activities for in-vehicle dialog systems, examine findings, discuss key challenges, and share our visions for voice-enabled interaction and intelligent assistance for smart vehicles over the…
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Automatic Speech Transcription for Low-Resource Languages — The Case of Yoloxóchitl Mixtec (Mexico)
In the present study, we focus exclusively on progress in developing speech recognition for the language of interest, Yoloxóchitl Mixtec (YM), an Oto-Manguean language spoken by fewer than 5000 speakers…
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The 2016 Speakers in the Wild Speaker Recognition Evaluation
This article provides details of the SITW speaker recognition challenge and analysis of evaluation results. We provide an analysis of some of the top performing systems submitted during the evaluation and…
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Fusion Strategies for Robust Speech Recognition and Keyword Spotting for Channel- and Noise-Degraded Speech
Current state-of-the-art automatic speech recognition systems are sensitive to changing acoustic conditions, which can cause significant performance degradation.
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Unsupervised Learning of Acoustic Units Using Autoencoders and Kohonen Nets
This work investigates learning acoustic units in an unsupervised manner from real-world speech data by using a cascade of an autoencoder and a Kohonen net.
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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…
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The Speakers in the Wild (SITW) Speaker Recognition Database
The Speakers in the Wild (SITW) speaker recognition database contains hand-annotated speech samples from open-source media for the purpose of benchmarking text-independent speaker recognition technology.
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Coping with Unseen Data Conditions: Investigating Neural Net Architectures, Robust Features, and Information Fusion for Robust Speech Recognition
This work investigates the performance of traditional deep neural networks under varying acoustic conditions and evaluates their performance with speech recorded under realistic background conditions that are mismatched with respect…
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Minimizing Annotation Effort for Adaptation of Speech-Activity Detection Systems
This paper focuses on the problem of selecting the best-possible subset of available audio data given a budgeted time for annotation.