Recent Improvements to Neural Network based Acoustic Modeling in the EML Transcription Platform
نویسنده
چکیده
In recent years, automatic speech recognition has enjoyed tremendous improvements from the use of (deep) neural networks (DNNs) for both acoustic modeling and stochastic language modeling [1, 2]. Powerful hardware, in particular graphics processing units (GPUs), and sophisticated training algorithms enable the use of deeper and deeper networks that reduce word error rates achieved with conventional Gaussian Mixture Models (GMM/HMM) by up to 30 percent [3]. However, comparisons of latency and real time factor for conventional and DNN based speech recognizers are only seldom published.
منابع مشابه
Persian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملشبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملDevelopment of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data
Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in p...
متن کاملDistillation Column Identification Using Artificial Neural Network
 Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...
متن کاملA Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment
In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...
متن کامل