Long short-term memory recurrent neural network architectures for Urdu acoustic modeling

Zia, Tehseen; Zahid, Usman

doi:10.1007/s10772-018-09573-7

Cited by 121 publications

(44 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…“Recurrent neural networks (RNNs) are a type of neural networks with cyclic connections. This configuration makes them a more potent instrument for sequence modeling than feed-forward neural networks,” (Zia and Zahid, 2019). …”

Section: Resultsmentioning

confidence: 99%

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Meza

Yepes

Rodrigo-Ilarri

et al. 2019

Heliyon

View full text Add to dashboard Cite

This study presents an analysis of three models associated with artificial intelligence as tools to forecast the generation of urban solid waste in the city of Bogotá, in order to learn about this type of waste's behavior. The analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a possible decision-making strategy was explored and implemented to plan and design technologies for the stages of collection, transport and final disposal of waste in cities, while taking into account their particular characteristics. The first model used to analyze data was the decision tree which employed machine learning as a non-parametric algorithm that models data separation limitations based on the learning decision rules on the input characteristics of the model. Support vector machines were the second method implemented as a forecasting model. The primary advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations among the same. Distribution by collection zone in the city, socio-economic stratification, population, and quantity of solid waste generated in a determined period of time were factors considered in the analysis of this forecast. The results found that support vector machines are the most appropriate model for this type of analysis.

show abstract

Section: Resultsmentioning

confidence: 99%

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Meza

Yepes

Rodrigo-Ilarri

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…The cell copies its own state and external input, connects the cell at the previous moment to the cell at the current moment, and determines when other cells are deleted or saved in memory. LSTM has achieved epoch-making results in natural language, time series, voice recognition and other fields, and is widely used [11][12].…”

Section: A Long and Short-term Memory Neural Networkmentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

View full text Add to dashboard Cite

Yield prediction has always been the focus of stock investors' attention in stock investment. This research uses the virtual stock scene constructed by VR technology to explore the mathematical model of return rate prediction. First, neural nodes, activation functions and output functions are designed under the LSTM framework, and a recurrent neural network that can handle long-term sequences is constructed. Use VR technology to simulate stock data and extract key frames from the image sequence. Aiming at the timing of key curve nodes, this study uses a long and short-term memory neural network that is good at processing time series data to classify curve trends, and realizes the identification of abnormal fluctuations in virtual scenes of stock images. Then, according to the existing probability control asset allocation theory, the index of investment allocation is adjusted from the entire time series to a dynamic time series. According to the stock price decomposition of EEMD, the EEMD-LSTM model is constructed to predict the stock market price. Use EEMD to decompose the original sequence to obtain IMF and trend items containing various timescale characteristics. Then, the LSTM neural network is used to predict and analyze each rate of return, and the analysis software Python is used to process and count the predicted results. At the same time, retrospective restrictions are regarded as riskier assets. Finally, the investment is made under conditions that do not allow shorting in the VR simulation scenario. The accuracy of the calculation is more than 70% except for the Shanghai and Shenzhen 300 Index reaching 66%. The research results show that in the case of different stock price forecasts, the prediction ability of LSTM neural network has been greatly improved compared with that of support vector machine, which fully reflects the advantages of LSTM in time series prediction.

show abstract

“…Further, boost in the performance is achieved by two layer LSTMs over Single Layer LSTMs but, the performance started decaying with further increment in LSTM layers. The LSTM architectures outperforms Gated Recurrent Unit Neural Network architectures [15].…”

Section: Literature Surveymentioning

confidence: 97%

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

2019

ijrte

View full text Add to dashboard Cite

A digit recognition system is built for recognizing the sequence of digits through 0-9. The system is experimented with speech corpus created in the room environment. The acoustic information to feature representation is achieved using PLP and MFCC features. The system initially utilized the conventional GMM-HMM framework, state of the art hybrid classifier with varied number of states to complete the speech recognition task, i.e., the system is first trained and tested using Monophone models, and system’s recognition accuracy is then evaluated using Triphone Models: Triphone1 models, which was later followed by Triphones2 models and Triphones3 Models. The Ngram Language model is used for both Monophone and Triphone training. The system performance is evaluated with the use of MFCC and PLP parameterisation techniques on Kaldi toolkit. The system performance is evaluated using metrics word error rate (WER) and Word Recognition Accuracy (WRA). The proposed system can be utilized for building speech applications

show abstract

Long short-term memory recurrent neural network architectures for Urdu acoustic modeling

Cited by 121 publications

References 18 publications

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Performance of Isolated and Continuous Digit Recognition System using Kaldi Toolkit

Contact Info

Product

Resources

About