Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: Systems and methods for reducing network traffic associated with a service. In some aspects, the systems and methods provide for using a first machine learning model to process a data stream for a communication with a user and generate a confidence score regarding whether to assign a communication suppression flag to the user account. Based on the confidence score not exceeding a first threshold, a communication suppression flag is not assigned to the user account. Based on the confidence score being between first and second thresholds, at least a portion of the data stream is extracted based on temporal proximity to a time stamp of an intent of the user to not receive further communications. Using a second machine learning model, the extracted portion of the data stream is processed to generate a prediction regarding whether to assign a communication suppression flag to the user account.

Abstract: Systems and computer-implemented methods disclosed herein relate to detecting errors in manually entered data. In one embodiment, the system can identify a named entity automatically from a conversation between a customer and service agent with a named entity recognition model that employs natural language processing and machine learning to detect a word or string of words in the conversation that corresponds to a named entity category. In another embodiment, the system can determine whether data entered into a field on a service platform by the service agent includes an error by comparing the data entered with the named entity. In another embodiment, the system can transmit an alert to the service agent through the service platform when there is a mismatch between the named entity and the data entered.

Abstract: A system and method for allowing a single live customer service agent to simultaneously serve multiple customers. According to various embodiments, a virtual agent operates at the front end to receive and attempt to handle customer issues. The virtual agent employs speech recognition and intent mapping in order to generate a proposed response that attempts to identify and resolve customer issues. In some scenarios, the proposed response includes both a response message and a response action to be taken. A chat history and the proposed response is then provided to the live agent. The live agent reviews the information provided, and determines whether the proposed response is appropriate. The live agent then approves the response or revises or replaces the response. The final response is sent back to the virtual agent for processing and providing to the customer.

Abstract: In some implementations, a system may capture audio from a call between a calling device and a called device. The system may filter the captured audio to generate a background audio layer. The system may generate an audio footprint that is a representation of sound in the background audio layer. The system may determine that the audio footprint includes a triggering sound footprint based on one or more audio characteristics of the audio footprint. The system may detect synthetic sound based on the audio footprint and after determining that the audio footprint includes the triggering sound footprint, wherein the synthetic sound is indicative of a sound recording. The system may transmit a notification to one or more devices associated with the call based on detecting the synthetic sound.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to correct ASR errors. The system may receive a transcription comprising transcribed word(s) and may determine whether the transcribed word(s) exceed associated predefined confidence level(s). Responsive to determining a transcribed word does not exceed a predefined confidence level, the system may generate a predicted word. The system may calculate a distance between numerical representations of the transcribed word and the predicted word and may determine whether the distance exceeds a predefined threshold. Responsive to determining the distance exceeds the predefined threshold, the system may determine whether at least one red flag word of a list of red flag words corresponds to a context of the transcription, and, responsive to making that determination, may classify the transcription as associated with a first category.

Abstract: Disclosed embodiments may include a system that may receive an audio file comprising an interaction between a first user and a second user. The system may detect, using a deep neural network (DNN), moment(s) of interruption between the first and second users from the audio file. The system may extract, using the DNN, vocal feature(s) from the moment(s) of interruption. The system may determine, using a machine learning model (MLM) and based on the vocal feature(s), whether a threshold number of moments of the moment(s) of interruption corresponds to a first emotion type. When the threshold number of moments corresponds to the first emotion type, the system may transmit a first message comprising a first binary indication. When the threshold number of moments do not correspond to the first emotion type, the system may transmit a second message comprising a second binary indication.

Abstract: A pitch detection method. Such a pitch detection method may have M-PWVT-TEO algorithm to detect a pitch value from a speech signal, apply a partial auto-correlation to a current signal with the pitch value to compensate the delay of the pitch value. Also, the pitch detection method may apply a full auto-correlation to the speech signal where the pitch value is not detected to recover on-sets of the speech signal.

Abstract: A pitch detection method. Such a pitch detection method may use a Teager Energy Operator (TEO) with a Pseudo Weigner Ville Transformation (PWVT) to recover speech from noise and to recover low-frequency information of the speech signal in its detection of a pitch value. Also, the pitch detection method may use the combinatory PWVT and the respective state machine for decision making.

Abstract: A pitch detection method. Such a pitch detection method may use Teager Energy Operator (TEO) and High Passed Filter (HPF) with Pseudo Weigner Ville Transformation (PWVT) to reduce the pitch deletion errors of highly correlated types of noises in a speech signal.

Abstract: Systems and methods for acoustic feedback cancellation. One method includes transmitting, with a transceiver, a known full-band sequence at a first time. The method includes receiving, via a microphone, a received audio signal including a received copy of the known full-band sequence. The method includes filtering the received audio signal to generate a filtered audio signal, and detecting, based on the filtered audio signal, a second portable communication device in proximity to the portable communication device. The method includes, in response to detecting the second portable communication device, determining, based on the first time and filtered audio signal, an estimated loop delay. The method includes initializing an adaptive feedback cancellation filter based on the estimated loop delay and the plurality of estimated filter coefficients.

Abstract: A pitch detection method. Such a pitch detection method may have M-PWVT-TEO algorithm to detect a pitch value from a speech signal, apply a partial auto-correlation to a current signal with the pitch value to compensate the delay of the pitch value. Also, the pitch detection method may apply a full auto-correlation to the speech signal where the pitch value is not detected to recover on-sets of the speech signal.

Abstract: A pitch detection method. Such a pitch detection method may apply Pseudo Weigner Ville Transformation (PWVT) as a spectral representation of speech signal. Also, the pitch detection method may take the median value of each frame of the speech signal as a threshold for making the voicing decision. Additionally, the pitch detection method may take a moving average of PWVT as the threshold for voicing decision.

Abstract: A pitch detection method. Such a pitch detection method may have M-PWVT-TEO algorithm to detect a pitch value from a speech signal, apply a partial auto-correlation to a current signal with the pitch value to compensate the delay of the pitch value. Also, the pitch detection method may apply a full auto-correlation to the speech signal where the pitch value is not detected to recover on-sets of the speech signal.

Abstract: A pitch detection method. Such a pitch detection method may use Teager Energy Operator (TEO) and High Passed Filter (HPF) with Pseudo Weigner Ville Transformation (PWVT) to reduce the pitch deletion errors of highly correlated types of noises in a speech signal.

Abstract: A pitch detection method. Such a pitch detection method may use a Teager Energy Operator (TEO) with a Pseudo Weigner Ville Transformation (PWVT) to recover speech from noise and to recover low-frequency information of the speech signal in its detection of a pitch value. Also, the pitch detection method may use the combinatory PWVT and the respective state machine for decision making.

Abstract: A pitch detection method. Such a pitch detection method may use a Teager Energy Operator (TEO) with a Pseudo Weigner Ville Transformation (PWVT) to recover speech from noise and to recover low-frequency information of the speech signal in its detection of a pitch value. Also, the pitch detection method may use the combinatory PWVT and the respective state machine for decision making.

Abstract: A pitch detection method. Such a pitch detection method may use Teager Energy Operator (TEO) and High Passed Filter (HPF) with Pseudo Weigner Ville Transformation (PWVT) to reduce the pitch deletion errors of highly correlated types of noises in a speech signal.

Abstract: A pitch detection method. Such a pitch detection method may apply Pseudo Weigner Ville Transformation (PWVT) as a spectral representation of speech signal. Also, the pitch detection method may take the median value of each frame of the speech signal as a threshold for making the voicing decision. Additionally, the pitch detection method may take a moving average of PWVT as the threshold for voicing decision.

Abstract: A pitch detection method. Such a pitch detection method may use Teager Energy Operator (TEO) and High Passed Filter (HPF) with Pseudo Weigner Ville Transformation (PWVT) to reduce the pitch deletion errors of highly correlated types of noises in a speech signal.