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Phoneme recognition using time-delay neural networks
The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: (1) using a three-layer arrangement of simple computingExpand
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Recognizing emotion in speech
The paper explores several statistical pattern recognition techniques to classify utterances according to their emotional content. The authors have recorded a corpus containing emotional speech withExpand
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Online handwriting recognition: the NPen++ recognizer
Abstract. This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on aExpand
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Language-independent and language-adaptive acoustic modeling for speech recognition
Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on theExpand
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A real-time face tracker
The authors present a real-time face tracker. The system has achieved a rate of 30+ frames/second using an HP-9000 workstation with a frame grabber and a Canon VC-Cl camera. It can track a person'sExpand
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Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-manyExpand
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A time-delay neural network architecture for isolated word recognition
Abstract A translation-invariant back-propagation network is described that performs better than a sophisticated continuous acoustic parameter hidden Markov model on a noisy, 100-speaker confusableExpand
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Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System?
Automatic evaluation metrics for Machine Translation (MT) systems, such as BLEU and the related NIST metric, are becoming increasingly important in MT. Yet, their behaviors are not fully understood.Expand
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Extracting deep bottleneck features using stacked auto-encoders
In this work, a novel training scheme for generating bottleneck features from deep neural networks is proposed. A stack of denoising auto-encoders is first trained in a layer-wise, unsupervisedExpand
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Modeling focus of attention for meeting indexing based on multiple cues
A user's focus of attention plays an important role in human-computer interaction applications, such as a ubiquitous computing environment and intelligent space, where the user's goal and intent haveExpand
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