ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2016 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics.
- Read more about The intrinsic value of HFO features as a biomarker of epileptic activity
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- Read more about Language Model Adaptation for ASR of Spoken Translations Using Phrase-based Translation Models and Named Entity Models
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- Read more about Recurrent neural networks for polyphonic sound event detection in real life recordings
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Slides from the presentation held at ICASSP 2016 for the paper: Recurrent neural networks for polyphonic sound event detection in real life recordings
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26 Views- Read more about Particle filters with independent resampling
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- Read more about Poster for Unsupervised User Intent Modeling by Feature-Enriched Matrix Factorization
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Spoken language interfaces are being incorporated into various devices such as smart phones and TVs. However, dialogue systems may fail to respond correctly when users’ request functionality is not supported by currently installed apps. This paper proposes a feature-enriched matrix factorization (MF) approach to model open domain intents, which allows a system to dynamically add unexplored domains according to users’ requests.
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- Read more about Poster for Zero-Shot Learning of Intent Embeddings for Expansion by Convolutional Deep Structured Semantic Models
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The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. However, the domain constraint along with the inflexible intent schema remains a big issue. This paper focuses on the task of intent expansion, which helps remove the domain limit and make an intent schema flexible. A convolutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances.
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22 Views- Read more about CLASSIFICATION OF BISYLLABIC LEXICAL STRESS PATTERNS IN DISORDERED SPEECH USING DEEP LEARNING
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- Read more about FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION
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Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have
developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our
poster.pdf
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7 Views- Read more about FILTERBANK LEARNING USING CONVOLUTIONAL RESTRICTED BOLTZMANN MACHINE FOR SPEECH RECOGNITION
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Convolutional Restricted Boltzmann Machine (ConvRBM) as a model for speech signal is presented in this paper. We have
developed ConvRBM with sampling from noisy rectified linear units (NReLUs). ConvRBM is trained in an unsupervised way to model speech signal of arbitrary lengths. Weights of the model can represent an auditory-like filterbank. Our
poster.pdf
poster.pdf (1108) - Categories:
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- Read more about Poster for Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
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