Abstract: A company may want to give access to voice conversations (e.g., a support call) to some users for review and analysis. However, the conversations may include personally identifiable information (PII), and the company wants to protect customer information while still allowing the use of the data. In one aspect, techniques are presented for receiving audio from the conversation and obtaining a redacted version of the audio, which does not include the PII, directly from the audio without having to rely on analyzing the transcript of the conversation first. Further, the modified audio may be deidentified to change the voice of the customer in the resulting audio in order to protect the customer identity.

Abstract: A lightning strike protection material for an aircraft includes an electrically-conductive grid with grid-forming members and nodes where grid-forming members overlap or intersect. A plurality of the grid-forming members and/or nodes include an outward-facing surface, and at least a portion of the outward-facing surface is concave.

Abstract: Disclosed herein is method of performing speech recognition using audio and visual information, where the visual information provides data related to a person's face. Image preprocessing identifies regions of interest, which is then combined with the audio data before being processed by a speech recognition engine.

Abstract: Disclosed herein is a deep learning model that can be used for performing speech or image processing tasks. The model uses multi-task training, where the model is trained for at least two inter-related tasks. For face detection, the first task is face detection (i.e. face or non-face) and the second task is facial feature identification (i.e. mouth, eyes, nose). The multi-task model improves the accuracy of the task over single-task models.

Abstract: Disclosed herein is a GPU-accelerated speech recognition engine optimized for faster than real time speech recognition on a scalable server-client heterogeneous CPU-GPU architecture, which is specifically optimized to simultaneously decode multiple users in real-time. In order to efficiently support real-time speech recognition for multiple users, a “producer/consumer” design pattern is applied to decouple speech processes that run at different rates in order to handle multiple processes at the same time. Furthermore, the speech recognition process is divided into multiple consumers in order to maximize hardware utilization. As a result, the platform architecture is able to process more than 45 real-time audio streams with an average latency of less than 0.3 seconds using one-million-word vocabulary language models.

Abstract: Disclosed herein is a deep learning model that can be used for performing speech or image processing tasks. The model uses multi-task training, where the model is trained for at least two inter-related tasks. For face detection, the first task is face detection (i.e. face or non-face) and the second task is facial feature identification (i.e. mouth, eyes, nose). The multi-task model improves the accuracy of the task over single-task models.

Abstract: Disclosed herein is a GPU-accelerated speech recognition engine optimized for faster than real time speech recognition on a scalable server-client heterogeneous CPU-GPU architecture, which is specifically optimized to simultaneously decode multiple users in real-time. In order to efficiently support real-time speech recognition for multiple users, a “producer/consumer” design pattern is applied to decouple speech processes that run at different rates in order to handle multiple processes at the same time. Furthermore, the speech recognition process is divided into multiple consumers in order to maximize hardware utilization. As a result, the platform architecture is able to process more than 45 real-time audio streams with an average latency of less than 0.3 seconds using one-million-word vocabulary language models.

Abstract: Disclosed herein is method of performing speech recognition using audio and visual information, where the visual information provides data related to a person's face. Image preprocessing identifies regions of interest, which is then combined with the audio data before being processed by a speech recognition engine.

Abstract: A method of optimizing the calculation of matching scores between phone states and acoustic frames across a matrix of an expected progression of phone states aligned with an observed progression of acoustic frames within an utterance is provided. The matrix has a plurality of cells associated with a characteristic acoustic frame and a characteristic phone state. A first set and second set of cells that meet a threshold probability of matching a first phone state or a second phone state, respectively, are determined. The phone states are stored on a local cache of a first core and a second core, respectively. The first and second sets of cells are also provided to the first core and second core, respectively. Further, matching scores of each characteristic state and characteristic observation of each cell of the first set of cells and of the second set of cells are calculated.

Abstract: A method of optimizing the calculation of matching scores between phone states and acoustic frames across a matrix of an expected progression of phone states aligned with an observed progression of acoustic frames within an utterance is provided. The matrix has a plurality of cells associated with a characteristic acoustic frame and a characteristic phone state. A first set and second set of cells that meet a threshold probability of matching a first phone state or a second phone state, respectively, are determined. The phone states are stored on a local cache of a first core and a second core, respectively. The first and second sets of cells are also provided to the first core and second core, respectively. Further, matching scores of each characteristic state and characteristic observation of each cell of the first set of cells and of the second set of cells are calculated.