Speech Data Mining Research Articles

Application of sensor-based speech data mining in E-commerce operations data analysis

With the rapid development of the e-commerce industry, enterprises need to effectively manage and analyze various operational data to support decision making and optimize business processes. Sensor technology is widely used in e-commerce environment, which can collect a lot of voice data and extract valuable information through data mining technology. The study presents the current background and trends of the e-commerce industry, with a focus on the importance and challenges of operational data analytics. A sensor-based voice data mining framework is proposed. Sensors are first used to collect voice data and turn it into a digital signal that can be used for analysis. Then the data mining algorithm is used to process and analyze the voice data, and a series of results about e-commerce operation are obtained. Analyze voice data to help companies develop effective marketing strategies, improve product quality, and optimize supply chain management. This study proves the role of sensor-based voice data mining in e-commerce operational data analysis. By harnessing the full potential of voice data, businesses can better understand and meet customer needs, improve competitiveness and performance. This research result has practical significance for the development of e-commerce industry, and provides guidance and reference for the research and practice in related fields.

Effects of Adaptive Prompts in Virtual Reality-Based Social Skills Training for Children with Autism.

The purpose of this single-case experimental design (SCED) study is to investigate how adaptive prompts in virtual reality (VR)-based social skills training affect the social skills performance of autistic children. Adaptive prompts are driven by autistic children's emotional states. To integrate adaptive prompts in VR-based training, we conducted speech data mining and endorsed micro-adaptivity design. We recruited four autistic children (12-13years) for the SCED study. We carried out alternating treatments design to evaluate the impacts of adaptive and non-adaptive prompting conditions throughout a series of VR-based social skills training sessions. Using mixed-method data collection and analyses, we found that adaptive prompts can foster autistic children's desirable social skills performance in VR-based training. Based on the study findings, we also describe design implications and limitations for future research.

Intelligent Voice System Design for Optimizing E-Business Advertising Rhetoric Based on SVM Algorithm.

With the emergence and development of artificial intelligence, design is no longer a process that can be completed only by creativity and knowledge. This article introduces research based on voice data mining and further discusses the optimization and application of rhetorical methods for e-commerce advertising. This article is working from two main aspects: one is to use association rule models in speech data mining to obtain useful rule relationships between Chinese prosody parameters, and the other is to use neural networks and data items in speech data mining. Using computer technology can make online advertisements interact with users. In addition to clicking on advertisements to jump to product pages, online advertisements also have multichannel sensory feelings, which can provide rich information and stimulate interest through interaction with users. We need to optimize the rhetoric of the advertisement and pay attention to the combination of the beauty of speech and the content of the advertisement when designing the advertisement. At the same time, we must also pay attention to the accuracy and elegance of the advertising language and wording.

Fusion effect of SVM in spark architecture for speech data mining in cluster structure

Fusion effect of SVM in the Spark architecture for speech data mining in cluster structure is studied in this manuscript. Based on the information entropy of nodes, the data in clusters are fused to eliminate redundant data and improve the efficiency of information fusion. Information entropy is a statistical form based on the characteristics of information representation, which reflects the average amount of information in information. Based on the Spark platform SVM algorithm, the frequent items with the highest support after each sort are directly recursively obtained, and the transaction data set is allocated to each computing node. The structure of the item head table directly affects the efficiency of the algorithm, so optimizing the structure of the item head table can improve the efficiency of the algorithm in constructing FP-Tree, and then improve the efficiency of the whole algorithm. The proposed speech data mining algorithm can cluster, analyze, and comprehensively detection the saliency information, the detection accuracy is much higher than the state-of-the-art models. The experimental results compared with the latest research have reflected that fact that the proposed model has the better performance and robustness.

A partition bagging ensemble learning algorithm for Parkinson's speech data mining

Methods for achieving diagnosis of Parkinson's disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.

Introduction to the Special Issue on Data Mining of Speech, Audio, and Dialog

ATA mining is concerned with the science, technology, and engineering of discovering patterns and extracting potentially useful or interesting information automatically or semi-automatically from data. Data mining was introduced in the 1990s and has deep roots in the fields of statistics, artificial intelligence, and machine learning. With the advent of inexpensive storage space and faster processing over the past decade or so, data mining research has started to penetrate new grounds in areas of speech and audio processing as well as spoken language dialog. It has been fueled by the influx of audio data that are becoming more widely available from a variety of multimedia sources including webcasts, conversations, music, meetings, voice messages, lectures, television, and radio. Algorithmic advances in automatic speech recognition have also been a major, enabling technology behind the growth in data mining. Current state-of-the-art, large-vocabulary, continuous speech recognizers are now trained on a record amount of data—several hundreds of millions of words and thousands of hours of speech. Pioneering research in robust speech processing, large-scale discriminative training, finite state automata, and statistical hidden Markov modeling have resulted in real-time recognizers that are able to transcribe spontaneous speech with a word accuracy exceeding 85%. With this level of accuracy, the technology is now highly attractive for a variety of speech mining applications. Speech mining research includes many ways of applying machine learning, speech processing, and language processing algorithms to benefit and serve commercial applications. It also raises and addresses several new and interesting fundamental research challenges in the areas of prediction, search, explanation, learning, and language understanding. These basic challenges are becoming increasingly important in revolutionizing business processes by providing essential sales and marketing information about services, customers, and product offerings. They are also enabling a new class of learning systems to be created that can infer knowledge and trends automatically from data, analyze and report application performance, and adapt and improve over time with minimal or zero human involvement. Effective techniques for mining speech, audio, and dialog data can impact numerous business and government applications. The technology for monitoring conversational speech to discover patterns, capture useful trends, and generate alarms is essential for intelligence and law enforcement organizations as well as for enhancing call center operation. It is useful for an

Semantic data mining of short utterances

This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While, in its simplest form, one can extend text data mining to speech data mining by using text tools on the output of a speech recognizer, we have found that it is not optimal. We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances. For the purposes of this paper, we examine semantic data mining in the context of semantic parsing and analysis in a specific situation involving the solution of a business problem that is known to the analyst. We are not attempting a generic semantic analysis of a set of speech. Our tools and methods allow the analyst to mine the speech data to discover the semantics that best cover the desired solution. The coverage, in this case, yields a set of Natural Language Understanding (NLU) classifiers that serve as testable hypotheses.

Special issue on data mining of speech, audio and dialog

Special issue on data mining of speech, audio and dialog

Special issue on data mining of speech, audio and dialog

Special issue on data mining of speech, audio and dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

Special issue on data mining of speech, audio and dialog

Special issue on data mining of speech, audio and dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

Special issue on data mining of speech, audio and dialog

Special issue on data mining of speech, audio and dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog

IEEE Transactions on Speech and Audio Processing Special Issue on Data Mining of Speech, Audio and Dialog