A conversion table generation device includes a similar word extraction unit and a conversion table generation unit. The similar word extraction unit is configured to extract similar words similar to first words, for each of the first words included in a word group used in a dialogue. The conversion table generation unit is configured to associate any one of the first words with the extracted similar words, that are similar to the plurality of first words, as second words, on the basis of the priority, and generates a conversion table for voice recognition with the second word as a conversion source and the first word as a conversion destination.

Artificial intelligence (Al) based technologies for improving patient appointment scheduling and inventory management are disclosed herein. An example method includes receiving, at a server including a natural language processing (NLP) model, an appointment request from a user. The example method further includes initiating, based on the appointment request, a patient appointment data stream including verbal responses from the user regarding an appointment of the user. The example method further includes applying, while simultaneously receiving the patient appointment data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations. The example method further includes querying a scheduling database to determine a matching appointment that satisfies a distance threshold, a date threshold, a service threshold, and an inventory threshold. The example method further includes causing a user device of the user to convey the matching appointment to the user.

The disclosure generally pertains to systems and methods for providing targeted content to users. In an example method, audio data can be received from a device. Sensor data associated with the device may also be received, and the sensor data may include location data. Upon receiving the audio data, an intent associated with the audio data can be determined. At least one of a product, service, or entity may be determined based on the intent. Content may then be determined based on the sensor data and at least one of the product, service, or entity. The content may be associated with a vehicle. An indication of the content can then be sent to the device.

An information output apparatus is realized that is capable of notifying information based on an input voice content. An information output apparatus according to the present embodiment includes an input unit to which data indicating a sound are input from an outside, a voice extraction unit that analyzes data which are input from the input unit and indicate a sound and that extracts data which indicate a voice emitted by a person, a vibration generation unit that generates vibration data, which are associated with data indicating a sound in advance set, based on a result of a comparison between the data which indicate the voice and are extracted by the voice extraction unit and the data indicating the sound in advance set, and a vibrator that vibrates based on the vibration data generated by the vibration generation unit.

The present invention provides a pronunciation error detection apparatus capable of following a text without the need for a correct sentence even when erroneous recognition such as a reading error occurs. The pronunciation error detection apparatus comprises: a speech recognition part that recognizes the speech in speech data based on a speech recognition model for a non-native speaker, and outputs speech recognition results, reliability and time information; a reliability determination part that outputs the speech recognition results with higher reliability than a predetermined threshold and the corresponding time information as the determined speech recognition results and the determined time information; and a pronunciation error detection part that outputs a phoneme as a pronunciation error when reliability for each phoneme in the speech recognition results using the native speaker speech recognition model under a weakly constraining grammar is greater than the reliability of the corresponding phoneme in the speech recognition results using the native speaker acoustic model under a constraining grammar in which the determined speech recognition results are correct for the speech data in a segment specified by the determined time information.

The present disclosure provides a call processing method, apparatus, electronic device and storage medium and relates to the field of cloud computing. The method may comprise: obtaining a calling subscriber's status information in real time while an intelligent dialogue robot is used to make a call with the calling subscriber; when it is determined that a call form of the intelligent dialogue robot needs to be adjusted, correspondingly adjusting the call form of the intelligent dialogue robot according to current status information of the calling subscriber. The solution of the present disclosure may be employed to improve the call performance of the intelligent dialogue robot.

A combination of propagation operations and learning algorithms is applied, using a selected set of labeled conversational logs retrieved from a subset of a plurality of conversational logs, to a remaining corpus of the plurality of conversational logs to train an automated response system according to an intent associated with each of the conversational logs. The combination of propagation operations and learning algorithms may include defining the labels by a user for the selected set of the subset of the plurality of conversational logs; training a probabilistic classifier using the defined labels of features of the selected set, wherein the probabilistic classifier produces labeling decisions for the subset of conversational logs; weighting the features of the selected set in a model optimization process; and/or training an additional classifier using the weighted features of the selected set and applying the additional classifier to the remaining corpus.

Disclosed is a voice recognition method and apparatus using artificial intelligence. A voice recognition method using artificial intelligence may include: generating a utterance by receiving a voice command of a user; obtaining a user's intention by analyzing the generated utterance; deriving an urgency level of the user on the basis of the generated utterance and prestored user information; generating a first response in association with the user's intention; obtaining main vocabularies included in the first response; generating a second response by using the main vocabularies and the urgency level of the user; determining a speech rate of the second response on the basis of the urgency level of the user; and outputting the second response according to the speech rate by synthesizing the second response to a voice signal.

In a method for handling out-of-sequence caller dialog, an intelligent voice interface is configured to lead callers through pathways of an algorithmic dialog that includes available voice prompts for requesting different types of caller information. The method may include, during a voice communication with a caller via a caller device, receiving from the caller device caller input data indicative of a voice input of the caller, without having first provided to the caller device any voice prompt that requests a first type of caller information, and determining, by processing the caller input data, that the voice input includes caller information of the first type. The method also includes after determining that the voice input includes the caller information of the first type, bypassing one or more voice prompts, of the available voice prompts, that request the first type of caller information.

Automated systems and methods are provided for processing natural language, comprising obtaining first and second digitally-encoded speech representations, respectively corresponding to an agent script for and a voice recording of a telecommunication interaction; generating a similarity structure based on the speech representations, the similarity structure representing a degree of semantic similarity between the speech representations; matching markers in the first speech representation to markers in the second speech representation based on the similarity structure; and dividing the telecommunication interaction into a plurality of sections based on the matching.