Embodiments of the present application provide a prediction method, device and system for rock mass instability stages, and belong to the technical field of rock mass instability prediction. The method includes the steps: acquiring acoustic emission signals of rock mass; extracting feature parameters from the acquired acoustic emission signals; and predicting instability stages of the rock mass in accordance with the feature parameters and a preset back propagation (BP) neural network model, wherein the preset BP neural network model is obtained by training a BP neural network and a genetic algorithm by virtue of the feature parameters of the acoustic emission signals at different rock mass instability stages. According to the technical solution in the present application, the problem in the training process of the BP neural network model that model parameter optimization may be easily trapped in a locally optimal solution is effectively solved.

An interactive response system mixes HSR subsystems with ASR subsystems to facilitate overall capability of user interfaces. The system permits imperfect ASR subsystems to nonetheless relieve burden on HSR subsystems. An ASR proxy is used to implement an IVR system, and the proxy dynamically selects one or more recognizers from a language model and a human agent to recognize user input. Selection of the one or more recognizers is based on factors such as confidence thresholds of the ASRs and availability of human resources for HSRs.

An interactive response system mixes HSR subsystems with ASR subsystems to facilitate overall capability of user interfaces. The system permits imperfect ASR subsystems to nonetheless relieve burden on HSR subsystems. An ASR proxy is used to implement an IVR system, and the proxy dynamically selects one or more recognizers from a language model and a human agent to recognize user input. Selection of the one or more recognizers is based on factors such as confidence thresholds of the ASRs and availability of human resources for HSRs.

A system, method, and apparatus for detecting drones are disclosed. An example method includes receiving a digital sound sample and partitioning the digital sound sample into segments. The method also includes applying a frequency and power spectral density transformation to each of the segments to produce respective sample vectors. For each of the sample vectors, the example method determines a combination of drone sound signatures and background sound signatures that most closely match the sample vector. The method further includes determining, for the sample vectors, if the drone sound signatures in relation to the background sound signatures that are included within the respective combinations are indicative of a drone. Conditioned on determining that the drone sound signatures are indicative of a drone, an alert message indicative of the drone is transmitted.

A system, method, and apparatus for detecting drones are disclosed. An example method includes receiving a digital sound sample and partitioning the digital sound sample into segments. The method also includes applying a frequency and power spectral density transformation to each of the segments to produce respective sample vectors. For each of the sample vectors, the example method determines a combination of drone sound signatures and background sound signatures that most closely match the sample vector. The method further includes determining, for the sample vectors, if the drone sound signatures in relation to the background sound signatures that are included within the respective combinations are indicative of a drone. Conditioned on determining that the drone sound signatures are indicative of a drone, an alert message indicative of the drone is transmitted.

A method and a device for waking up via a speech based on artificial intelligence are provided in the present disclosure. The method includes: clustering phones to select garbage phones for representing the phones; constructing an alternative wake-up word approximate to a preset wake-up word according to the preset wake-up word; constructing a decoding network according to the garbage phones, the alternative wake-up word and the preset wake-up word; and waking up via the speech by using the decoding network. Due to the data size for the garbage phones is significantly smaller than the data size for the garbage words, a problem that the data size occupied is too large by using a garbage word model in the prior art is solved. Meanwhile, as a word is composed of several phones, the garbage phones may be more likely to cover all words than the garbage words. Thus, an accuracy of waking up is improved and a probability of false waking up is reduced.

Technologies for detecting an end of a sentence in automatic speech recognition are disclosed. An automatic speech recognition device may acquire speech data, and identify phonemes and words of the speech data. The automatic speech recognition device may perform a syntactic parse based on the recognized words, and determine an end of a sentence based on the syntactic parse. For example, if the syntactic parse indicates that a certain set of consecutive recognized words form a syntactically complete and correct sentence, the automatic speech recognition device may determine that there is an end of a sentence at the end of that set of words.

Technologies for detecting an end of a sentence in automatic speech recognition are disclosed. An automatic speech recognition device may acquire speech data, and identify phonemes and words of the speech data. The automatic speech recognition device may perform a syntactic parse based on the recognized words, and determine an end of a sentence based on the syntactic parse. For example, if the syntactic parse indicates that a certain set of consecutive recognized words form a syntactically complete and correct sentence, the automatic speech recognition device may determine that there is an end of a sentence at the end of that set of words.

There are provided a method and a system for automatically generating a back-channel in an interactive agent system. According to an embodiment of the disclosure, an automatic back-channel generation method includes: predicting a back-channel by analyzing an utterance of a user inputted in a back-channel prediction model; and generating the predicted back-channel, and the back-channel prediction model is an AI model that is trained to predict a back-channel to express from the utterance of the user. Accordingly, a back-channel is automatically generated by utilizing a back-channel prediction module which is based on a language model, so that a natural dialogue interaction with a user may be implemented in an interactive agent system, and quality of a dialogue service provided to a user may be enhanced.

There are provided a method and a system for automatically generating a back-channel in an interactive agent system. According to an embodiment of the disclosure, an automatic back-channel generation method includes: predicting a back-channel by analyzing an utterance of a user inputted in a back-channel prediction model; and generating the predicted back-channel, and the back-channel prediction model is an AI model that is trained to predict a back-channel to express from the utterance of the user. Accordingly, a back-channel is automatically generated by utilizing a back-channel prediction module which is based on a language model, so that a natural dialogue interaction with a user may be implemented in an interactive agent system, and quality of a dialogue service provided to a user may be enhanced.