A method for coordinating actions between an audio channel and a synchronized non-audio channel includes receiving an indication of a start of a session associated with a user and having an audio channel that is synchronized with a non-audio channel. Thereafter, repeated determinations are made as to whether a prompt on the non-audio channel has been received from the user. In response to each determination that the prompt on the non-audio channel has not been received from the user, a signal is sent to cause an inaudible output on the audio channel to the user. In response to a determination that the prompt on the non-audio channel has been received from the user, an audible output is selected based on an activity by the user on the non-audio channel, and a signal is sent to cause the audible output to be output on the audio channel.

A remote control device includes a digital audio storage device, a talk button, and an optical distance measurer. The digital audio storage device is configured to continually record an audio input for a specific amount of time. The talk button is coupled to the digital audio storage device and is configured to initiate a transmission of the audio input to a set-top box device. The optical distance measurer is coupled to the talk button and is configured to automatically measure a distance to a user in response to the talk button being pressed.

A remote control device includes a digital audio storage device, a talk button, and an optical distance measurer. The digital audio storage device is configured to continually record an audio input for a specific amount of time. The talk button is coupled to the digital audio storage device and is configured to initiate a transmission of the audio input to a set-top box device. The optical distance measurer is coupled to the talk button and is configured to automatically measure a distance to a user in response to the talk button being pressed.

According to one embodiment, a signal processing apparatus correlates a plurality of communication terminals as a group and enables one-to-many communications in the group. The signal processing apparatus includes processing circuitry. The processing circuitry assigns a transmission right to one of the communication terminals in the group. The processing circuitry generates text data based on voice data from said one of the communication terminals in possession of the transmission right. The processing circuitry gives a texting completion notice indicative of completion of texting processing to the communication terminals in the group. The processing circuitry transmits, after the texting completion notice is given, the generated text data to at least one of the communication terminals in the group.

Disclosed embodiments are directed an application program configured to run on a user's mobile device can allow voice-activated call pick-up to the user, without the user having to use his or her hands for picking up the call. For example, the application program can initially be trained to a user's voice command. When an incoming call is received at the mobile device, the user can pick up the call by issuing a voice command. In some embodiments, the application program can determine whether to allow voice-activated pick-up of calls based on data collected from multiple sensors associated with the vehicle, the mobile device, or a remote source.

Disclosed embodiments are directed an application program configured to run on a user's mobile device can allow voice-activated call pick-up to the user, without the user having to use his or her hands for picking up the call. For example, the application program can initially be trained to a user's voice command. When an incoming call is received at the mobile device, the user can pick up the call by issuing a voice command. In some embodiments, the application program can determine whether to allow voice-activated pick-up of calls based on data collected from multiple sensors associated with the vehicle, the mobile device, or a remote source.

A shovel includes a lower traveling body, an upper turning body turnably mounted on the lower traveling body, a cab mounted on the upper turning body, a display device provided in the cab and configured to display a setting screen associated with work assistance, an audio input device provided in the cab, and a hardware processor configured to perform audio recognition. The hardware processor is configured to recognize speech input through the audio input device and executes a process related to the setting screen according to a recognition result.

The present invention provides artificial intelligence technology which has machine-learning-based information understanding capability, including metric learning providing improved classification performance, classification of an object considering a semantic relationship, understanding of the meaning of a scene based on the metric learning and the classification, and the like. An electronic device according to one embodiment of the present invention comprises a memory in which at least one instruction is stored, and a processor for executing the stored instruction. Here, the processor extracts feature data from training data of a first class, obtains a feature point by mapping the extracted feature data to an embedding space, and makes an artificial neural network learn in a direction for reducing a distance between the obtained feature point and an anchor point.

A contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.

A contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.