ARTIFICIAL INTELLIGENCE-BASED SYSTEM AND METHOD FOR SPEECH RECOGNITION SERVICES

Abstract

The present invention discloses an Artificial Intelligence (AI)-based system and method for speech recognition services, integrating advanced AI algorithms with Internet of Things (IoT) devices. The system features a multi-layered architecture comprising an AI Processing Unit (AIPU) built on an Application-Specific Integrated Circuit (ASIC) for executing deep learning algorithms, edge devices with noise-canceling microphones, and a distributed sensor network for contextual data collection. The IoT devices capture and pre-process audio signals in real-time, enhancing recognition accuracy through beamforming and noise reduction techniques. Hardware accelerators such as GPUs and TPUs supplement the AIPU, ensuring high-speed processing. The system supports low-latency communication and utilizes AI models for multilingual speech-to-text conversion. Additionally, the system features self-learning capabilities, environmental adaptability, and robust data security. This novel integration of AI and IoT enables efficient, accurate, and scalable speech recognition services across various applications, from smart homes to industrial automation.

Specification

Description:[014] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[015] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[016] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[017] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[018] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[019] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[020] In an embodiment of the invention and referring to Figures 1, the present invention relates to a sophisticated system and method for speech recognition services utilizing an advanced Artificial Intelligence (AI) framework integrated with Internet of Things (IoT) devices. The system is designed to offer high-accuracy speech-to-text conversion, language translation, and contextual understanding by leveraging cutting-edge AI algorithms and specialized hardware components. This invention addresses the limitations of conventional speech recognition systems, particularly in noisy environments, by incorporating novel hardware features alongside state-of-the-art software solutions.
[021] The system comprises several interconnected hardware and software components that work in unison to deliver efficient speech recognition services. The architecture includes a multi-layered AI processing unit, edge IoT devices, noise-canceling microphones, a distributed sensor network, and specialized processing modules. The system is built on a robust framework where data flow between these components is optimized to enhance speech recognition accuracy and response time.
[022] At the core of the system is the AI Processing Unit (AIPU), which is built on an Application-Specific Integrated Circuit (ASIC). This unit is responsible for executing deep learning algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The AIPU accelerates the processing of audio signals by utilizing parallel computing techniques and reducing latency in real-time applications. The hardware design is optimized for energy efficiency, making it suitable for both portable and stationary deployments.
[023] The system integrates IoT-enabled edge devices that serve as the primary data collectors. These devices include smart microphones embedded with microelectromechanical system (MEMS) sensors, which capture high-fidelity audio signals. The captured audio is pre-processed at the edge, where initial noise reduction and feature extraction are performed. The edge devices are connected via a low-latency wireless network (e.g., Wi-Fi 6 or 5G) to ensure seamless communication with the central AI server.
[024] To enhance speech recognition in noisy environments, the system incorporates advanced noise-canceling microphones with beamforming technology. These microphones utilize multiple arrays to isolate the speaker's voice from background noise, providing clean input to the AIPU. The beamforming algorithm dynamically adjusts the directionality of the microphone arrays to focus on the target audio source, thus improving signal-to-noise ratio (SNR).
[025] A distributed sensor network is deployed to monitor environmental conditions such as temperature, humidity, and ambient noise levels. These sensors are connected to the IoT framework and provide contextual data that aids in optimizing the speech recognition process. For example, the system can adjust its noise cancellation settings based on real-time ambient noise data to enhance recognition accuracy.
[026] The AIPU is supplemented by hardware accelerators, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), which are used for complex AI model computations. These accelerators enhance the speed of training and inference processes, allowing the system to adapt to new languages and accents more efficiently. The hardware accelerators are connected to the main processing unit via high-speed interfaces like PCIe 5.0.
[027] The system includes a high-capacity memory and storage subsystem, comprising DDR5 RAM and NVMe-based Solid-State Drives (SSDs). This configuration ensures rapid access to large datasets required for AI model training and inference. Additionally, Non-Volatile Memory Express over Fabrics (NVMe-oF) technology is employed to reduce latency in accessing cloud storage resources.
[028] The software layer of the system is built on AI models optimized for speech recognition, including Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) models. These models are fine-tuned using Transfer Learning techniques to enhance their accuracy in diverse linguistic environments. The AI algorithms leverage the specialized hardware accelerators to deliver real-time speech-to-text conversion.
[029] The communication between IoT devices and the central AI server is established using MQTT and CoAP protocols, which are lightweight and suitable for low-power devices. These protocols ensure secure and efficient data transmission, supporting encrypted communication through Transport Layer Security (TLS).
[030] The system is designed for real-time data processing using an event-driven architecture. A distributed data pipeline, implemented using Apache Kafka, handles the high-throughput audio streams from multiple IoT devices. This ensures scalable and fault-tolerant data ingestion, allowing the system to operate seamlessly even under high load conditions.
[031] To minimize latency, the system employs edge computing techniques, where pre-trained AI models are deployed on edge devices for initial processing. This reduces the amount of data transmitted to the central server, thereby optimizing bandwidth usage and improving response times for critical applications such as emergency voice commands.
[032] The system features a dynamic resource allocation mechanism that utilizes Kubernetes for container orchestration. This allows the system to scale resources up or down based on the workload, ensuring optimal performance and cost efficiency. The orchestration layer also supports multi-cloud deployment, enabling redundancy and high availability.
[033] Given the diverse deployment environments, the system includes a smart power management system powered by AI algorithms. This system dynamically adjusts the power consumption of each hardware component based on usage patterns and battery levels, thus extending the operational lifespan of portable devices.
[034] The user interface is designed to provide real-time feedback on speech recognition accuracy. The system includes a touchscreen interface that displays the transcribed text, along with confidence scores and translation options. Additionally, haptic feedback is incorporated for visually impaired users.
[035] An AI-based noise filtering algorithm is implemented to enhance the clarity of audio input. This algorithm uses a combination of spectral subtraction and machine learning techniques to filter out non-stationary noise sources, making it highly effective in dynamic environments like public spaces or industrial settings.
[036] The system supports voice authentication as a security feature, utilizing a combination of voice biometrics and AI-driven pattern recognition. This ensures secure access to the system by verifying the unique vocal characteristics of authorized users. Data encryption is applied to all stored and transmitted data to protect user privacy.
[037] To enhance data security, the system includes a hardware-based Trusted Platform Module (TPM) that ensures the integrity of the system firmware and encryption keys. This module provides a secure boot process, protecting the system from unauthorized firmware modifications.
[038] The system is designed to integrate seamlessly with cloud services such as AWS, Azure, or Google Cloud. This integration supports cloud-based AI model updates, remote diagnostics, and predictive maintenance. The cloud platform also provides an additional layer of computational resources for intensive AI tasks.
[039] The integration of AI and IoT is achieved through a unified data model that enables interoperability across different IoT devices. The system uses AI to analyze the data collected from IoT sensors, providing actionable insights such as adjusting the sensitivity of speech recognition based on environmental noise levels.
[040] The system incorporates self-learning capabilities using Reinforcement Learning (RL) algorithms. These algorithms allow the system to improve its performance over time by adapting to user preferences and environmental changes. The self-learning mechanism is crucial for personalized user experiences, especially in multi-user environments.
[041] The efficacy of the proposed system is validated through comparative performance analysis, as shown in Table 1. The system demonstrates superior performance in terms of accuracy, latency, and noise resilience when compared to existing solutions.

[042] The modular design of the system ensures scalability and extensibility, allowing for easy integration of additional IoT devices and AI models. The use of containerized microservices ensures that new features can be deployed without disrupting existing services.
[043] This invention is applicable in various domains, including smart homes, automotive voice control, healthcare diagnostics, and industrial automation. The system's robustness and adaptability make it suitable for deployment in both consumer and enterprise settings.
[044] The proposed system for AI-based speech recognition services demonstrates a significant advancement in the field of human-machine interaction. By leveraging novel hardware components, IoT integration, and advanced AI algorithms, the invention offers a highly accurate and efficient solution for real-world applications. The synergy between the hardware and software components ensures optimal performance, setting a new standard for speech recognition technologies. , Claims:1. An Artificial Intelligence-based system for speech recognition services comprising:
a) an AI Processing Unit (AIPU) constructed on an Application-Specific Integrated Circuit (ASIC), the AIPU configured to execute deep learning algorithms including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for processing audio signals;
b) a plurality of IoT-enabled edge devices, each comprising noise-canceling microphones integrated with microelectromechanical system (MEMS) sensors for capturing audio signals, said edge devices configured to pre-process the captured audio signals for initial noise reduction and feature extraction;
c) a distributed sensor network interconnected with the IoT devices, providing contextual data including environmental conditions such as temperature, humidity, and ambient noise levels;
d) a high-capacity memory and storage subsystem for storing data and model parameters necessary for speech recognition and processing;
e) a communication protocol that enables secure and low-latency data transmission between the IoT devices and the central AI server using MQTT or CoAP protocols.
2. The system as claimed in claim 1, wherein the AIPU is supplemented by hardware accelerators including Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs) to accelerate the execution of AI algorithms and reduce latency during speech recognition tasks.
3. The system as claimed in claim 1, further comprising a real-time data processing pipeline, wherein distributed event-driven architecture is implemented using Apache Kafka to handle high-throughput audio streams from multiple IoT devices.
4. The system as claimed in claim 1, wherein the edge devices employ beamforming technology and dynamic microphone array directionality to isolate a target audio source from background noise, thereby enhancing signal-to-noise ratio (SNR) for accurate speech recognition.
5. The system as claimed in claim 1, wherein the AI algorithms further include Natural Language Processing (NLP) models and Automatic Speech Recognition (ASR) models, optimized for multilingual speech-to-text conversion through Transfer Learning techniques.
6. The system as claimed in claim 5, wherein the AI models are deployed on the edge devices to perform initial speech recognition tasks, thereby reducing latency and bandwidth consumption by processing audio data locally before sending it to the central AI server.
7. The system as claimed in claim 1, wherein the distributed sensor network further includes ambient noise level sensors that dynamically adjust the noise-canceling settings of the microphones based on real-time data from the sensor network.
8. The system as claimed in claim 1, wherein the communication protocol employs Transport Layer Security (TLS) for encrypted communication between the IoT devices and the central server to ensure data privacy and security.
9. The system as claimed in claim 1, wherein the hardware accelerators are interconnected with the AIPU through high-speed interfaces such as PCIe 5.0 to enable high-bandwidth data transfer and faster AI model inference.
10. The system as claimed in claim 1, wherein the system includes a self-learning mechanism utilizing Reinforcement Learning (RL) algorithms that allow the system to adapt to user preferences and environmental conditions for continuous improvement in speech recognition accuracy over time.

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