AUTOMATED SYSTEM FOR ASSESSING ENGLISH LANGUAGE PROFICIENCY USING SPEECH ANALYSIS

Abstract

The present invention discloses an automated system for assessing English language proficiency using advanced speech analysis techniques. The system includes a central processing unit (CPU) integrated with a Digital Signal Processor (DSP) for real-time speech analysis, a specialized microphone array with beamforming technology to capture clear voice inputs, and an Analog-to-Digital Converter (ADC) for high-fidelity digitization. A Noise Cancellation Module (NCM) enhances speech clarity by reducing background noise, while a Speech Recognition Processor (SRP) with FPGA acceleration transcribes spoken words into text. The Speech Analysis Engine (SAE), powered by Convolutional Neural Networks (CNNs), evaluates phonetics, fluency, grammar, and vocabulary. The system also features a Voice Biometrics Module (VBM) for authentication, a Language Adaptation Unit (LAU) for accent recognition, and a high-resolution touchscreen interface for user interaction. This comprehensive, hardware-optimized solution delivers accurate, scalable, and real-time language proficiency assessments for various applications.

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 an Automated System for Assessing English Language Proficiency Using Speech Analysis, designed to evaluate and grade English language proficiency based on spoken inputs. The system integrates advanced hardware and software components to deliver an accurate and efficient assessment of a user's language skills. It encompasses a series of novel hardware units and sensor systems that enhance the system's capability, making it a comprehensive solution for language proficiency testing.
[021] The system comprises a central processing unit (CPU) integrated with a Digital Signal Processor (DSP) specifically designed for high-speed audio processing. This combination allows real-time analysis of speech input, ensuring minimal latency and accurate assessment. The DSP is configured to handle the pre-processing of audio signals, including noise reduction, echo cancellation, and voice enhancement, which are critical for accurate language assessment.
[022] A specialized microphone array is employed to capture the user's speech. This microphone array is equipped with multiple directional microphones arranged in a circular pattern, allowing the system to capture voice inputs from different directions while minimizing ambient noise. The array incorporates beamforming technology, which enhances the clarity of the captured speech by focusing on the source of the sound and reducing background interference.
[023] The system includes an Analog-to-Digital Converter (ADC) unit that digitizes the analog speech signals captured by the microphone array. The ADC is optimized for high fidelity, ensuring that the digital representation of the speech is accurate and free of distortions. This unit is crucial for maintaining the integrity of the input signal as it transitions from the analog domain to the digital processing environment.
[024] To further enhance the speech capture capabilities, the invention includes a Noise Cancellation Module (NCM) that operates at both the hardware and firmware levels. The NCM uses adaptive filtering techniques to distinguish between speech and non-speech components of the input signal, effectively reducing background noise. This module significantly improves the quality of the speech signal before it is processed by the core assessment algorithms.
[025] The system features a dedicated Speech Recognition Processor (SRP) that utilizes advanced machine learning models to transcribe spoken words into text. The SRP is equipped with a Field-Programmable Gate Array (FPGA) to accelerate the recognition process, enabling faster response times and more accurate transcription. The FPGA is programmed to execute neural network inference, optimizing the balance between speed and accuracy.
[026] A high-resolution Touchscreen Interface is integrated into the system to provide a user-friendly means of interaction. This interface allows users to initiate tests, receive instructions, and view their assessment results. The touchscreen is connected to the main processing unit via a Serial Peripheral Interface (SPI) bus, ensuring fast data transmission and a responsive user experience.
[027] The core of the system's functionality lies in its Speech Analysis Engine (SAE), which utilizes a combination of hardware accelerators and proprietary algorithms to evaluate various aspects of spoken language. The SAE is capable of analyzing phonetics, intonation, fluency, grammar, and vocabulary usage. It uses a multi-layered Convolutional Neural Network (CNN) model to extract features from the speech signal and assess language proficiency accurately.
[028] An integral part of the system is its Data Storage Unit (DSU), which is implemented using solid-state drives (SSDs) for high-speed data access. The DSU stores all the speech data, user profiles, and assessment results. It uses encryption algorithms to protect sensitive user data, ensuring compliance with privacy standards. The DSU is connected to the CPU via a high-speed SATA interface to facilitate rapid data retrieval and storage.
[029] For enhanced mobility and versatility, the system is equipped with a Bluetooth module, allowing it to connect wirelessly to external devices such as headsets, smartphones, or tablets. This module uses the latest Bluetooth Low Energy (BLE) technology to minimize power consumption while maintaining a stable connection. The Bluetooth module is integrated with a microcontroller that manages wireless communications efficiently.
[030] A Power Management Unit (PMU) is designed to optimize the system's power usage. The PMU uses a combination of a lithium-ion battery and a supercapacitor to provide a stable power supply, even in the event of sudden power fluctuations. The PMU monitors and adjusts the power distribution to different hardware components based on their real-time requirements, thus enhancing the overall efficiency of the system.
[031] The system includes a Thermal Management System (TMS) that utilizes multiple heat sensors and a fan-based cooling mechanism to prevent overheating during intensive processing tasks. The TMS is controlled by a dedicated microcontroller that adjusts the fan speed based on temperature readings, thereby ensuring optimal operating conditions for all hardware components.
[032] The Speech Analysis Engine is further enhanced by a Voice Biometrics Module (VBM), which uses unique vocal characteristics to authenticate users. This module leverages a hardware-based neural network accelerator to analyze the user's voice pattern, adding an additional layer of security and personalization to the language assessment process.
[033] To support a wide range of users, the system is multilingual and can be configured to assess proficiency in different dialects and accents of the English language. It utilizes a Language Adaptation Unit (LAU) that automatically adjusts the assessment algorithms based on the user's accent, making the system more inclusive and accurate for non-native speakers.
[034] The system's architecture is designed for scalability, with a modular hardware framework that allows for easy upgrades and customization. Each hardware component is connected via a high-speed bus network, allowing seamless communication and data exchange between units. The system uses a custom-designed motherboard with multiple expansion slots for future hardware upgrades.
[035] An embedded Neural Processing Unit (NPU) is utilized to handle the computational demands of the machine learning models used in the assessment process. The NPU accelerates the execution of deep learning algorithms, significantly reducing the time required for speech analysis and improving the accuracy of the assessment.
[036] The system supports cloud integration, allowing for remote storage and analysis of speech data. A built-in Wi-Fi module enables secure connectivity to cloud platforms, where advanced analytics can be performed. This feature is particularly useful for institutions that require centralized assessment and reporting.
[037] Table 1 below illustrates the system's performance in terms of accuracy and processing speed across different hardware configurations. The data shows that the use of hardware accelerators such as the DSP, FPGA, and NPU significantly enhances the system's efficiency.

[038] The invention includes a Feedback Analysis Unit (FAU) that provides detailed feedback to users based on their speech performance. This unit uses natural language processing (NLP) techniques to generate constructive feedback, helping users improve their language skills over time.
[039] A novel feature of this system is the Integration Sensor Hub (ISH), which consolidates inputs from various hardware sensors, such as the microphone array, temperature sensors, and power usage monitors. The ISH optimizes the data flow and ensures synchronized operation of all hardware components.
[040] The system is built on a robust embedded operating system that manages hardware resources, provides real-time task scheduling, and ensures seamless operation of the speech assessment modules. The operating system is optimized for low-latency performance and supports multi-threaded processing, essential for handling complex speech analysis tasks.
[041] An LED Indicator Module is included to provide visual cues during the assessment process. The module uses a series of colored LEDs to indicate the system's status, such as recording in progress, assessment complete, or error detection. This enhances the user experience by providing clear and immediate feedback.
[042] The system incorporates a Hardware Encryption Module (HEM) to protect user data during transmission and storage. The HEM uses AES-256 encryption, ensuring that all sensitive information is securely handled, making the system compliant with data protection regulations.
[043] To validate the efficacy of the system, a series of user tests were conducted across different proficiency levels, ranging from beginners to advanced speakers. Table 2 below demonstrates the correlation between the system's assessments and human evaluations, showcasing the system's reliability.

[044] In conclusion, the Automated System for Assessing English Language Proficiency Using Speech Analysis leverages a combination of state-of-the-art hardware components and machine learning algorithms to deliver an efficient, accurate, and user-friendly solution for language assessment. The novel integration of specialized hardware units, such as DSPs, NPUs, microphone arrays, and secure data modules, positions this invention as a significant advancement in the field of language proficiency testing. , Claims:1. An automated system for assessing English language proficiency using speech analysis, comprising:
a) a central processing unit (CPU) integrated with a Digital Signal Processor (DSP) for real-time speech input analysis;
b) a specialized microphone array with multiple directional microphones configured in a circular pattern, equipped with beamforming technology to capture and enhance voice signals from a user;
c) an Analog-to-Digital Converter (ADC) optimized for high-fidelity digitization of the speech signals;
d) a Noise Cancellation Module (NCM) that uses adaptive filtering techniques to reduce background noise at both hardware and firmware levels;
e) a Speech Recognition Processor (SRP) incorporating a Field-Programmable Gate Array (FPGA) for accelerated transcription of spoken words into text;
f) a Speech Analysis Engine (SAE) utilizing Convolutional Neural Networks (CNN) to evaluate phonetics, intonation, fluency, grammar, and vocabulary;
g) a Data Storage Unit (DSU) with solid-state drives (SSDs) connected via a high-speed SATA interface for secure storage of speech data, user profiles, and assessment results; and
h) a user interface with a high-resolution touchscreen connected via a Serial Peripheral Interface (SPI) bus for initiating tests and displaying results.
2. The system as claimed in Claim 1, wherein the Noise Cancellation Module (NCM) further includes a dual-stage adaptive filter capable of distinguishing between voice signals and ambient noise to enhance the clarity of the captured speech.
3. The system as claimed in Claim 1, wherein the Speech Recognition Processor (SRP) is configured with a Neural Processing Unit (NPU) to optimize the execution of deep learning algorithms, thereby reducing processing time and enhancing the accuracy of speech-to-text conversion.
4. The system of Claim 1, further includes a Voice Biometrics Module (VBM) that uses vocal characteristics for user authentication, implemented with a hardware-based neural network accelerator to analyze unique voice patterns.
5. The system as claimed in Claim 1, wherein the Speech Analysis Engine (SAE) includes a Language Adaptation Unit (LAU) that dynamically adjusts assessment algorithms to accommodate different accents and dialects of the English language, enhancing accuracy for non-native speakers.
6. The system as claimed in Claim 1, further includes a Bluetooth Low Energy (BLE) module integrated with a microcontroller to enable wireless connectivity with external devices, optimizing power consumption and maintaining a stable communication link.
7. The system as claimed in Claim 1, wherein the Power Management Unit (PMU) uses a combination of lithium-ion batteries and supercapacitors to provide stable power, with a monitoring circuit that adjusts power distribution to optimize performance under variable load conditions.
8. The system as claimed in Claim 1, further includes a Thermal Management System (TMS) with multiple heat sensors and a fan-based cooling mechanism, controlled by a microcontroller to prevent overheating and maintain optimal operational temperatures.
9. The system as claimed in Claim 1, wherein the Data Storage Unit (DSU) is equipped with encryption algorithms for securing sensitive data using AES-256 encryption, ensuring compliance with data protection standards during transmission and storage.
10. The system as claimed in Claim 1, further includes an Integration Sensor Hub (ISH) that consolidates data from various sensors, including microphone arrays, temperature sensors, and power monitors, to optimize system performance by ensuring synchronized operation of all components.

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