Speech recognition with feedback from natural language processing for adaptation of acoustic model

2008 ◽  
Vol 123 (1) ◽  
pp. 25
Author(s):  
Hitoshj Honda
Keyword(s):  
Natural Language Processing ◽  
Speech Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Acoustic Model

The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

2020 ◽  
Vol 9 (2) ◽  
pp. 5-9
Author(s):  
Oksana Chulanova
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Natural Language Processing ◽  
Decision Support ◽  
Speech Recognition ◽  
Human Resources ◽  
Natural Language ◽  
Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

A Voice Calculator Based on Speech Error Correction

2013 ◽  
Vol 846-847 ◽  
pp. 1239-1242
Author(s):  
Yang Yang ◽  
Hui Zhang ◽  
Yong Qi Wang
Keyword(s):  
Natural Language Processing ◽  
Numerical Calculation ◽  
Speech Recognition ◽  
Natural Language ◽  
Error Correction ◽  
Recent Work ◽  
Language Processing ◽  
Speech Error ◽  
Parsing Algorithm ◽  
Recognition Errors

This paper presents our recent work towards the development of a voice calculator based on speech error correction and natural language processing. The calculator enhances the accuracy of speech recognition by classifying and summarizing recognition errors on numerical calculation speech recognition area, then constructing Pinyin-text-mapping library and replacement rules, and combing priority correction mechanism and memory correction mechanism of Pinyin-text-mapping. For the expression after correctly recognizing, the calculator uses recursive-descent parsing algorithm and synthesized attribute computing algorithm to calculate the final result and output the result using TTS engine. The implementation of this voice calculator makes a calculator more humane and intelligent.

scholarly journals Ontology-Based Question Answering System for an Academic Domain

2021 ◽  
Author(s):  
García-Robledo Gabriela A ◽  
Reyes-Ortiz José A ◽  
González-Beltrán Beatriz A ◽  
Bravo Maricela
Keyword(s):  
Natural Language Processing ◽  
User Interface ◽  
Speech Recognition ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Question Answering ◽  
Question Answering System ◽  
Methods And Techniques

The development of question answering (QA) systems involves methods and techniques from the areas of Information Extraction (EI), Natural Language Processing (NLP), and sometimes speech recognition. A user interface that involves all these tasks requires deep development to improve the interaction between a user and a device. This paper describes a Spanish QA system for an academic domain through a multi-platform user interface. The system uses a voice query to be transformed into text. The semi-structured query is converted into SQWRL language to extract a system of ontologies from an academic domain using patterns. The answer of the ontologies is placed in templates classified according to the type of question. Finally, the answer is transformed into a voice. A method for experimentation is presented focusing on the questions asked in voice and their respective answers by experts from the academic domain in a set of 258 questions, obtaining a 92% accuracy.

scholarly journals Multiple-Weight Recurrent Neural Networks

2017 ◽  
Author(s):  
Zhu Cao ◽  
Linlin Wang ◽  
Gerard de Melo
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Speech Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Experimental Results ◽  
Great Success ◽  
Human Ability

Recurrent neural networks (RNNs) have enjoyed great success in speech recognition, natural language processing, etc. Many variants of RNNs have been proposed, including vanilla RNNs, LSTMs, and GRUs. However, current architectures are not particularly adept at dealing with tasks involving multi-faceted contents. In this work, we solve this problem by proposing Multiple-Weight RNNs and LSTMs, which rely on multiple weight matrices in an attempt to mimic the human ability of switching between contexts. We present a framework for adapting RNN-based models and analyze the properties of this approach. Our detailed experimental results show that our model outperforms previous work across a range of different tasks and datasets.

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