A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

Embodiments of this application provide a real-time voice call control method performed by an electronic device. The method includes: obtaining a mixed call voice in real time during a cloud conference call, where the mixed call voice includes at least one branch voice; determining energy information corresponding to each frequency point of the call voice in a frequency domain; determining an energy proportion of each branch voice at each frequency point in total energy of the frequency point based on the energy information at the frequency point; determining a quantity of branch voices comprised in the call voice based on the energy proportion of each branch voice at each frequency point; and controlling the voice call by setting a call voice control manner based on the quantity of branch voices.

Embodiments of this application provide a real-time voice call control method performed by an electronic device. The method includes: obtaining a mixed call voice in real time during a cloud conference call, where the mixed call voice includes at least one branch voice; determining energy information corresponding to each frequency point of the call voice in a frequency domain; determining an energy proportion of each branch voice at each frequency point in total energy of the frequency point based on the energy information at the frequency point; determining a quantity of branch voices comprised in the call voice based on the energy proportion of each branch voice at each frequency point; and controlling the voice call by setting a call voice control manner based on the quantity of branch voices.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

In various embodiments, this application provides an audio information processing method, an audio information processing apparatus, an electronic device, and a storage medium. An audio information processing method in an embodiment includes: obtaining a first audio feature corresponding to audio information; performing, based on an audio feature at a specified moment in the first audio feature and audio features adjacent to the audio feature at the specified moment, an encoding on the audio feature at the specified moment to obtain a second audio feature corresponding to the audio information; obtaining decoded text information corresponding to the audio information; and obtaining, based on the second audio features and the decoded text information, text information corresponding to the audio information. According to this method, fewer parameters are used in the process of obtaining the second audio feature and obtaining, based on the second audio feature and the decoded text information, the text information corresponding to the audio information, thereby reducing computational complexity in the audio information processing process and improving audio information processing efficiency.

A score integration unit 7 obtains a new score Score (l.sub.1:n.sup.b, c) that integrates a score Score (l.sub.1:n.sup.b, c) and a score Score (w.sub.1:o.sup.b, c). This new score Score (l.sub.1:n.sup.b, c) becomes a score Score (l.sub.1:n.sup.b) in a hypothesis selection unit 8. Thus, the score Score (l.sub.1:n.sup.b) can be said to take into account the score Score (w.sub.1:o.sup.b, c). In a speech recognition apparatus, first information is extracted on the basis of the score Score (l.sub.1:n.sup.b) taking into account the score Score (w.sub.1:o.sup.b, c). Thus, speech recognition with higher performance than that in the related art can be achieved.

A score integration unit 7 obtains a new score Score (l.sub.1:n.sup.b, c) that integrates a score Score (l.sub.1:n.sup.b, c) and a score Score (w.sub.1:o.sup.b, c). This new score Score (l.sub.1:n.sup.b, c) becomes a score Score (l.sub.1:n.sup.b) in a hypothesis selection unit 8. Thus, the score Score (l.sub.1:n.sup.b) can be said to take into account the score Score (w.sub.1:o.sup.b, c). In a speech recognition apparatus, first information is extracted on the basis of the score Score (l.sub.1:n.sup.b) taking into account the score Score (w.sub.1:o.sup.b, c). Thus, speech recognition with higher performance than that in the related art can be achieved.

A speech recognition apparatus (2000) includes a first model (10) and a second model (20). The first model (10) is learned by training data with an audio frame as input data, and with, as correct answer data, compressed character string data acquired by encoding character string data represented by the audio frame. The second model (20) is a learned decoder (44) acquired by learning an autoencoder (40) being constituted of an encoder (42) converting input character string data into compressed character string data, and the decoder (44) converting, into character string data, the compressed character string data output from the encoder. The speech recognition apparatus (2000) inputs an audio frame to the first model (10), inputs, to the second model (20), compressed character string data output from the first model (10), and thereby generates character string data corresponding to the audio frame.