A dialogue system, a vehicle and a method for controlling the vehicle is disclosed. The method for controlling the vehicle includes: acquiring an utterance and a speech pattern by recognizing a speech when a speech of a plurality of speakers is input through a speech input device; classifying dialogue contents for each speaker based on the acquired utterance and speech pattern; acquiring a relationship between the speakers based on the acquired utterance; understanding an intention and a context for each speaker based on the acquired relationship between the speakers and the acquired dialogue content for each speaker determining an action corresponding to the acquired relationship and the acquired intention and context for each speaker, and outputting an utterance corresponding to the determined action; generating a control command corresponding to the determined action; and controlling a load based on the generated control command.

Dialogue system, vehicle and method for controlling the vehicle

Research on teaching based on tennis-assisted robot image recognition

In a method, a computer and a medical computer for automatic failure analysis in order to provide a cause of failure of the medical imaging apparatus during operation, input data are read into the computer that include raw data or image data, acquired by the imaging apparatus. A set of performance indicators in the input data is calculated by the computer. A trained neural network system is accessed with the calculated performance indicators, in order to provide result data that, in the case of a failure, identify a failure source.

Automatic failure detection in magnetic resonance apparatuses

Methods, computer program products, and systems are presented. The methods include, for instance: collecting various word data from cross-domain sources and subject websites; assessing relevancy of feature vectors from external domains, live content of subject websites, and secondary terms derived from the live contents; expanding a language model for a domain by relevance passing a logistic regression threshold.

Domain terminology expansion by relevancy

The application of information technology has realized the transformation of people’s production and lifestyle and also promoted the development of the sports industry. At present, the application of informatization in the field of sports is becoming more and more powerful. By using the advanced methods and technologies of its information display, this paper aimed to realize the research on the tracking of tennis sports video objects in the mobile network environment. It is helpful to analyze and solve the objectivity problems such as most of the loopholes in the single research and traditional methods of tennis video target tracking research. By drawing on the principles and rules of machine learning algorithms, the tennis video target tracking research is carried out, and the informatization and dataization of tennis are realized. In the experiment of the target tracking algorithm, 12 tracking videos have higher tracking accuracy than other parameters. The overall tracking accuracy of the video sequence under grayscale feature was 0.694; the tracking accuracy was the highest, but the tracking speed was the lowest compared to other parameters in the experiment. Therefore, it is very important to study the target tracking of tennis sports in the tennis field.

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Tennis Video Target Tracking Based on Mobile Network Communication and Machine Learning Algorithm

Methods, computer program products, and systems are presented. The methods include, for instance: determining that one or more word of a feature vector more supports than negates a language model corresponding to the domain based on a sensitivity of respective word. Words having acceptable sensitivities are added to the language model, and the language model is enhanced by use of machine learning in order to accurately and comprehensively model the language specific for the domain.

Domain terminology expansion by sensitivity

Automatic speech recognition (ASR) systems deployed to sporting events generate subtitles to help people understand the sports action when they are in a noisy environment or if they are hearing impaired. The signal quality can be degraded due to crowd noise, emotional outbursts, and other sounds from sports play. Different accents from sports players around the world, in combination with different terminologies and slang, cause challenges for ASR systems. The use of our custom online supervised learning method provides a learning mechanism to enable ASR systems to adapt to sporting domains. In this paper, we discuss the results of a novel language model expansion for the 2016 U.S. Open Tennis Championships. Words in the corpora are expanded by hypernyms (which provide broader meaning to a target word) and hyponyms (which provide specific meaning) of a synset, or groups of words that are synonyms. The candidate word is filtered on the basis of relevancy predictors. The contextual evidence provides our language model with "understanding" of tennis words to decrease the word error rate by 5.41% and to increase word confidence by 6.71%. Subtitling corrections by human annotators provide online learning for our language model. Transcribed videos from previous U.S. Open tournaments and from YouTube are used to evaluate all experiments.

Using language models for improving speech recognition for U.S. Open Tennis Championships

Aspects of the present invention provide devices that subtitle streaming video with audio and identify a speaker in a streaming video with audio according to words spoken by the speaker matched to a cognitive print. The cognitive print includes traits classified according a hierarchical long short term model (LSTM). The hierarchical LSTM includes layers of LSTMs and each layer corresponds to the classification of one trait. A processor annotates a subtitle of the words spoken by the speaker, which decorates the subtitle with a label representative of the identified speaker, and streams the decorated subtitle with the streaming video with audio.

Cognitive print speaker modeler

A set of vectors may be obtained. The vectors may be multi-dimensional vectors that are associated with and describe tokens from a first set of tokens from a corpus of sources. The description may be based in part on the relationship of the token to at least a portion of the remainder of the corpus. A set of sentiment scores may be obtained. The sentiment scores in the set of sentiment scores may describe a sentiment associated with a corresponding token that is described by a vector from the set of vectors. The set of vectors and the set of sentiment scores may be input into a pattern-recognizer pathway in a first neural network. A probability value of a potential future event may then be generated by the first neural network. The probability value may be based on the set of vectors and the set of sentiment scores.

Predictive neural network with sentiment data

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