Abstract: A chatbot maintains a conversation with a user by providing a plurality of separate conversational tiers that allow the chatbot to interact with the user, each of the conversational tiers having a set of production rules that are independent of production rules for other ones of the tiers. The production rules indicate a flow of conversation between the user and the chatbot. The chatbot selects one of the conversational tiers based on previous conversational tiers used by the chatbot in connection with conversing with the user and based on content of conversations between the user and the chatbot. The chatbot responds to statements of the user according to a specific production rule that is chosen by the chatbot based on a particular one of the conversational tiers that has been selected and based on other factors. A scripting engine may match statements of the user with specific production rules.

Abstract: An automatic speech recognition (ASR) system includes at least one processor and a memory storing instructions.

Abstract: A system and method to receive a spoken utterance and convert the spoken utterance into a recognized speech results through an automatic speech recognition service. A spoken utterance into a recognized speech result through an automatic speech recognition service. The recognized speech results are interpreted through a natural language processing module. A normalizer processes the recognized speech results that transforms the recognized speech interpretations into predefined form for a given automatic speech recognition domain and further determines which automatic speech recognition domains or the recognized speech results are processed by a dedicated dialogue management proxy module or a conversation module.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory, a microphone and a processor configured to compare a volume of a voice input through the microphone and a standard voice volume stored in the memory, corresponding to a space in which the electronic apparatus is located, and identify whether to perform a voice recognition on the voice based on the comparison.

Abstract: The present disclosure provides a method, computer system and computer program product facilitating generation of a conversation content. A computer-implemented method comprises: obtaining, by a device operatively coupled to one or more processors, history conversation data of an user; determining, by the device, a professional level of the user based on the history conversation data; and generating, by the device, a conversation content for the user based on the professional level of the user.

Abstract: Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.

Abstract: A provider computing system includes a service recommendation database configured to retrievably store recommendation information, a network interface configured to communicate data over a network, and a processing circuit that includes a processor and memory. The memory is structured to store instructions that are executable by the processor and cause the processing circuit to receive a first set of recommendation information derived from a first set of voice data received by a local voice assistant and store the first set of recommendation information in the services recommendation database. The processing circuit is further caused to receive a request to generate a recommendation from a user voice assistant, derived from a second set of voice data received by the user voice assistant. The processing circuit is also caused to access the first set of recommendation information in response to the request and transmit the recommendation to the user voice assistant.

Abstract: Methods and apparatus for automated processing of natural language text is described. The text can be preprocessed to produce language-space data that includes descriptive data elements for words. Source code that includes linguistic expressions, and that may be written in a programming language that is user-friendly to linguists, can be compiled to produce finite-state transducers and bi-machine transducers that may be applied directly to the language-space data by a language-processing virtual machine. The language-processing virtual machine can select and execute code segments identified in the finite-state and/or bi-machine transducers to disambiguate meanings of words in the text.

Abstract: In one embodiment, a system includes a microphone, memory, and a processor. The processor is operable to access environmental sounds captured by the microphone while the user is watching a live video. The processor is further operable to determine, by analyzing the captured sounds, a response by the user to the live video and generate a comment or a reaction element based on the determined response by the user to the live video. The processor is further operable to post the generated comment or reaction element for display along with the live video.

Abstract: The present disclosure provides a speech data processing method and apparatus, a device and a storage medium, wherein the method comprises: a client obtains speech data, uses a speech wakeup engine to perform wakeup recognition for the obtained speech data, and stores the obtained speech data in a cyclic buffer queue; the client obtains a wakeup recognition result returned by the speech wakeup engine upon completion of the wakeup recognition, and if the wakeup recognition result is acknowledged wakeup and it is determined that there occurs a case of oneshot currently, determines an interception starting position and obtains buffer data in the cyclic buffer queue from the interception staring position to an end of the queue, the buffer data at least including the content to be recognized; the client sends the buffer data to a speech recognition engine so that the speech recognition engine performs speech recognition for the content to be recognized in the buffer data.

Abstract: When speech of a first user includes a first word that is stored in a memory and associated with the first user, it is determined whether or not a difference between a first time and a second time is equal to or less than a predetermined time. The first time is a current time at which the first user spoke the first word. The second time is a time at which a second user last spoke a second word associated with the first word. When the difference between the first time and the second time is equal to or less than the predetermined time, a speaker outputs speech of a same content associated with the first word and the second word.

Abstract: The present disclosure discloses a method and an apparatus for judging termination of sound reception and a terminal device. The method including: performing a voice activity detection on a current sound clip to obtain a first value; performing a semantic relevance detection on the current sound clip and a next sound clip by deep learning to obtain a second value; performing a weighted calculation on the first value and the second value to obtain a third value; comparing the third value with a preset threshold; and determining whether sound reception of the current sound clip is terminated based on the comparison result.

Abstract: A method for acoustic detection of shooter location includes the following steps: receiving acoustic signals by a microphone array; detecting muzzle blast (MB) and shock wave (SW) signals through matched filter and cross correlation processes; transforming the detected MB and SW signals from time domain into frequency domain; beamforming the signals by means of the Delay and Sum method in frequency domain; estimating the direction of arrival (DOA) for the MB and SW signals by finding the azimuth and elevation which give the maximum power of the beamforming response; performing range estimation using the difference between the arrival time of the MB and SW signals together with the DOA estimations.

Abstract: A translation device includes a first receiver, a second receiver, a first output unit, and a second output unit. The first receiver receives a first voice and converts the first voice into a first voice signal. The second receiver receives a second voice and converts the second voice into a second voice signal. The first output unit outputs a voice corresponding to second translation data generated based on the second voice signal. The second output unit outputs a voice corresponding to first translation data generated based on the first voice signal. The first receiver is disposed near a mouth of a first user. The first output unit is disposed at a position at which the first output unit is in contact with an ear of the first user. The second receiver and the second output unit are disposed on a head of the first user.

Abstract: Wireless communication devices are adapted to facilitate scalable synchronization channels in a wireless communications system. According to one example, a wireless communication device may identify a reference synchronization channel including a reference bandwidth and a reference numerology, where the reference numerology includes a reference subcarrier spacing and a reference cyclic prefix. The reference synchronization channel may be scaled according to a type of communication, resulting in a scaled synchronization channel including a scaled bandwidth and a scaled numerology compatible with the communication type. According to another example, a wireless communication device may determine a communication type employed by the device for wireless communications, and may search for a particular scaled synchronization channel based on the determined communication type. Other aspects, embodiments, and features are also included.

Abstract: The present disclosure relates to processing a plurality of audio signals. The method includes receiving the plurality of audio signals in the frequency domain and determining an overall attenuation multiplier based on the plurality of audio signals and an overall lookup table that relates decibel values to different overall attenuation multipliers. The method further includes determining an attenuation vector comprising a plurality of bin-specific attenuation multipliers, each bin-specific attenuation multiplier respectively corresponding to a different frequency bin of the plurality of frequency bins. The method further includes scaling each bin-specific attenuation value in the attenuation vector with the overall attenuation multiplier, and editing each of the audio signals based on the scaled bin-specific attenuation values in the attenuation vector.

Abstract: A haptic device comprises a signal generator that is configured to receive an input word that is a unit of a language. The signal generator converts the input word into one or more phonemes of the input word. The signal generator further converts the one or more phonemes into a sequence of actuator signals. The sequence of actuator signals is formed from a concatenation of sub-sequences of actuator signals. Each phoneme corresponding to a unique sub-sequence of actuator signals. The haptic device further comprises a two dimensional array of cutaneous actuators configured to receive the sequence of actuator signals from the signal generator, each of the actuator signals mapped to a cutaneous actuator of the two dimensional array of cutaneous actuators.

Abstract: A cluster includes a server cluster and a worker computer cluster. Each worker computer included in the worker computer cluster separately obtains a word and at least one context word of the word that are extracted from a corpus. The worker computer obtains word vectors for the word and the at least one context word. The worker computer calculates a gradient according to the word, the at least one context word, and the word vectors. The worker computer asynchronously updates the gradient to a server included in the server cluster. The server updates the word vectors for the word and the at least one context word of the word according to the gradient.

Abstract: A device includes a receiver and a decoder. The receiver is configured to receive bitstream parameters corresponding to at least an encoded mid signal. The decoder is configured to generate a synthesized mid signal based on the bitstream parameters. The decoder is also configured to generate one or more upmix parameters. An upmix parameter of the one or more upmix parameters having a first value or a second value based on determining whether the bitstream parameters correspond to an encoded side signal. The first value is based on a received downmix parameter. The second value is based at least in part on a default parameter value. The decoder is further configured to generate an output signal based on the synthesized mid signal and the one or more upmix parameters.

Abstract: An electronic device is provided. The electronic apparatus includes a communicator; a storage configured to store a channel map including a channel name, and a broadcasting frequency and a channel number corresponding to the channel name; and a processor to extract a first keyword from a first voice command and search the channel map for the first keyword, store the first keyword, extract a second keyword from a second voice command within a predetermined period of time of the first voice command, and search the channel map for the second keyword in response to the first keyword not being found in the channel map, and generate a control command to select a channel number corresponding to the second keyword in the channel map and add the first keyword as a pseudo channel name for the selected channel number in response to the second keyword being found in the channel map.