Speech categorization using recurrent networks
نویسندگان
چکیده
منابع مشابه
Continuous Mandarin speech recognition using hierarchical recurrent neural networks
An ANN-based continuous Mandarin base-syllable recognition system is proposed. It adopts a hybrid approach to combine an HRNN with a Viterbi search. The HRNN is taken as a frond-end processor and responsible for calculating discrimination scores for all 411 base-syllables. The Vi-terbi search is then followed to nd out the best base-syllable sequence with highest score as the recognized output....
متن کاملAudio Visual Speech Recognition Using Deep Recurrent Neural Networks
In this work, we propose a training algorithm for an audiovisual automatic speech recognition (AV-ASR) system using deep recurrent neural network (RNN).First, we train a deep RNN acoustic model with a Connectionist Temporal Classification (CTC) objective function. The frame labels obtained from the acoustic model are then used to perform a non-linear dimensionality reduction of the visual featu...
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملEfficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks
Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...
متن کاملUnfolded recurrent neural networks for speech recognition
We introduce recurrent neural networks (RNNs) for acoustic modeling which are unfolded in time for a fixed number of time steps. The proposed models are feedforward networks with the property that the unfolded layers which correspond to the recurrent layer have time-shifted inputs and tied weight matrices. Besides the temporal depth due to unfolding, hierarchical processing depth is added by me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of the Acoustical Society of America
سال: 1988
ISSN: 0001-4966
DOI: 10.1121/1.2025398