FRAMEWORK FOR TEACHING ENGLISH PRONUNCIATION USING PHONETIC PATTERNS

Abstract

The present disclosure provides a method for teaching English pronunciation using phonetic patterns. Spoken English input from a learner is detected. Phonetic segments from said input are analyzed against pre-established phonetic pattern templates. Deviations in said phonetic segments from said templates are identified based on acoustic feature comparisons. Corrective feedback is generated, providing guidance for modulating said deviations to align with standard English phonetic pronunciation. Said corrective feedback is provided to said learner through an auditory or visual medium. Said learner is prompted to modify pronunciation either in real-time or during a subsequent session.

Specification

Description:Field of the Invention


The present disclosure generally relates to language learning systems. Further, the present disclosure particularly relates to methods for teaching English pronunciation using phonetic patterns.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
The field of language learning, particularly English pronunciation, has seen various approaches to improving learners' pronunciation skills. Traditional methods often rely on face-to-face instruction where learners receive guidance from language instructors through repeated exercises and drills. Such methods emphasize mimicry and repetition, wherein learners attempt to reproduce sounds made by the instructor. These approaches, although widely used, are limited by subjective feedback provided by instructors, which can vary based on teaching style and instructor experience. Additionally, traditional methods may not efficiently address individual phonetic deviations that learners face due to regional accents or speech patterns.
Further, automated language learning systems have been developed to overcome the limitations associated with traditional, instructor-led methods. Some existing systems use prerecorded audio exercises, which guide learners to practice pronunciation. In such systems, learners listen to an audio sample and attempt to replicate said sample. While prerecorded exercises provide consistent feedback, such systems are generally passive in nature, lacking real-time interaction and feedback. Consequently, learners often fail to recognize phonetic deviations in real-time, which hinders effective pronunciation improvement.
Moreover, prior art systems have introduced interactive software-based tools that incorporate speech recognition technology. In such systems, spoken input from learners is recorded and compared to pre-set reference pronunciations. The software then provides feedback based on deviations from said reference pronunciations. However, the accuracy of such systems is often compromised due to the inability to capture subtleties of speech, such as stress, intonation, and accent variations. These systems typically focus on isolated phonemes rather than providing comprehensive feedback on connected speech patterns, further limiting the scope of pronunciation correction.
Another approach in the prior art uses phonetic pattern analysis techniques, which attempt to analyze acoustic properties of speech to provide corrective feedback. In such systems, phonetic segments from the learner's speech are analyzed using phonetic templates, and feedback is generated based on the analysis. However, such systems are often ineffective due to a lack of real-time feedback mechanisms and an inability to adapt to individual learning needs. Moreover, prior art systems frequently suffer from technical limitations in identifying subtle deviations in learners' pronunciation, particularly when it comes to stress, rhythm, and intonation in longer phrases or sentences.
Additionally, some prior art systems utilize visual aids, such as spectrograms and waveforms, to provide visual feedback on pronunciation. Learners are expected to interpret said visual aids to adjust their speech. However, such systems rely heavily on the learner's ability to understand complex visual data, making them less effective for beginners or non-technical learners. The lack of real-time auditory feedback and reliance on visual representation detracts from the effectiveness of such systems for language learners who require immediate guidance to correct their speech.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for teaching English pronunciation using phonetic patterns.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
An objective of the present disclosure is to provide an effective method for teaching English pronunciation by utilizing phonetic patterns. The system of the present disclosure aims to address the need for real-time feedback and accurate phonetic correction based on phonetic segment analysis.
In an aspect, the present disclosure provides a method for teaching English pronunciation, wherein phonetic segments from spoken English input are detected from a learner. Said phonetic segments are analyzed against pre-established phonetic pattern templates. Deviations from said templates are identified based on acoustic features. Corrective feedback is generated to guide learners in modulating pronunciation to align with standard English pronunciation. Such corrective feedback is provided through auditory or visual media, enabling learners to modify pronunciation in real-time or during subsequent sessions.
The method enables improved pronunciation through a real-time corrective process. Further, the method supports the selection of preferred regional accents and provides feedback through various media, including auditory, visual, and haptic devices, enhancing learner engagement. Dynamic adjustments based on progression, interactive exercises, and progress reports further improve learning outcomes.

Brief Description of the Drawings


The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a method for teaching English pronunciation using phonetic patterns, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates decision based diagram of a method for teaching English pronunciation using phonetic patterns, in accordance with the embodiments of the present disclosure.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms "a" and "an" and "the" and "at least one" and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term "at least one" followed by a list of one or more items (for example, "at least one of A and B") is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
As used herein, the term "phonetic segments" refers to individual units of sound within spoken language, often corresponding to specific phonemes or combinations of phonemes produced by a speaker. Such phonetic segments are detected within the speech of a learner and serve as the fundamental building blocks for analyzing pronunciation patterns. Said phonetic segments may include consonants, vowels, diphthongs, and other sound elements that contribute to the overall pronunciation of words and sentences in English. Additionally, phonetic segments reflect the speaker's articulation, voicing, and acoustic features such as pitch and duration. Phonetic segments are crucial in identifying variations in pronunciation and are used as a reference point in comparing spoken input to pre-established phonetic pattern templates. Said detection is performed using methods that capture the acoustic properties of said phonetic segments in real time, enabling the generation of corrective feedback to guide learners in improving their pronunciation accuracy based on said detected segments.
As used herein, the term "pre-established phonetic pattern templates" refers to a set of predefined acoustic patterns that represent standard pronunciation models for phonetic segments in English. Such templates serve as benchmarks for comparing phonetic segments detected within the spoken input of a learner. Said templates encompass a wide range of phonetic variations, including different regional accents, stress patterns, and intonation. Additionally, said phonetic pattern templates may include both segmental features, such as individual phonemes, and suprasegmental features, such as the rhythm, stress, and intonation of longer speech units. Such templates are derived from databases that incorporate standard pronunciation data from native speakers and serve as a tool for identifying deviations in the learner's pronunciation. By analyzing the learner's phonetic segments against said templates, the method identifies pronunciation errors, which are addressed through corrective feedback aimed at aligning the learner's speech with said standard phonetic patterns.
As used herein, the term "acoustic features" refers to measurable properties of sound, including frequency, amplitude, duration, and formant structure, which characterize the pronunciation of phonetic segments. Said acoustic features are extracted from spoken English input to compare the learner's pronunciation with pre-established phonetic pattern templates. Such features provide critical information regarding the articulation of phonemes, including whether the pronunciation involves proper voicing, tongue placement, and airflow control. Acoustic features also encompass suprasegmental elements like intonation and stress, which influence the overall rhythm and melody of speech. The method analyzes said acoustic features to identify specific deviations between the learner's spoken input and the standard phonetic templates. Said analysis forms the basis for generating corrective feedback, which assists the learner in adjusting pronunciation to align more closely with standard English speech patterns, including both phonetic accuracy and natural intonation.
As used herein, the term "corrective feedback" refers to responses generated based on the analysis of the learner's pronunciation, intended to guide the learner toward more accurate English pronunciation. Said corrective feedback provides specific instructions for modulating the learner's detected phonetic deviations to match standard pronunciation patterns. Such feedback may include auditory guidance, visual cues, or tactile sensations, depending on the medium through which the feedback is delivered. Corrective feedback can focus on segmental elements, such as improving the articulation of individual phonemes, or suprasegmental elements, such as correcting rhythm and stress patterns. By addressing the specific deviations identified in the analysis, said corrective feedback enables the learner to make real-time adjustments during pronunciation practice or modify said pronunciation in subsequent sessions, thereby improving overall speech clarity and accuracy.
As used herein, the term "auditory or visual medium" refers to the channels through which corrective feedback is provided to the learner. Said medium may include auditory feedback, where the learner receives spoken instructions or examples of correct pronunciation, or visual feedback, where graphical representations, such as waveforms or mouth diagrams, are displayed to guide pronunciation adjustments. In auditory feedback, the learner hears comparisons between detected phonetic segments and the correct phonetic patterns, while in visual feedback, phonetic deviations are highlighted through visual elements, such as color-coded waveforms or spectrograms. Said media enable real-time interaction between the learner and the pronunciation system, facilitating immediate correction of detected phonetic deviations during practice sessions or enabling delayed correction for future learning sessions. Both media play a key role in enhancing the learner's ability to perceive and modify pronunciation errors effectively.
FIG. 1 illustrates a method for teaching English pronunciation using phonetic patterns, in accordance with the embodiments of the present disclosure. In an embodiment, detecting phonetic segments within spoken English input from a learner involves capturing spoken input through a microphone or other suitable device capable of receiving audio signals. Said spoken input is processed to extract individual phonetic segments, which represent the smallest units of sound in the language, such as vowels, consonants, and diphthongs. Said detection process includes segmenting said input into identifiable sound units using acoustic analysis techniques, wherein key characteristics of the sound, such as frequency, intensity, and duration, are used to define boundaries between phonetic segments. Said detection may be achieved using signal processing methods to analyze temporal changes in said spoken input and identify transitions between different phonetic characteristics. The output of said detection process is a sequence of phonetic segments that correspond to distinct units of speech as spoken by said learner, which is subsequently analyzed in further stages.
In an embodiment, analyzing said phonetic segments against pre-established phonetic pattern templates involves comparing detected phonetic segments to a set of reference templates representing standard pronunciation patterns. Said phonetic pattern templates are generated based on acoustic features of native speakers and may include a variety of regional accents or pronunciations to accommodate different learner preferences. Each detected phonetic segment is evaluated for features, including pitch, formant frequencies, and duration, and said features are compared against corresponding features within said templates. Said analysis is used to determine how accurately said learner's pronunciation matches the standard patterns represented by said phonetic pattern templates. During said analysis, both segmental features, such as individual phonemes, and suprasegmental features, such as stress and intonation, are assessed to gain a comprehensive understanding of pronunciation accuracy.
In an embodiment, identifying deviations in said phonetic segments from said phonetic pattern templates based on a comparison of acoustic features involves determining specific differences between said detected segments and said standard templates. Said deviations may include discrepancies in articulation, such as incorrect placement of the tongue, improper voicing, or deviations in pitch and intonation patterns. Identifying said deviations is performed by measuring the extent to which acoustic features of said learner's spoken segments differ from said features of said phonetic pattern templates. Such features may include, but are not limited to, frequency characteristics, timing, rhythm, and amplitude of said spoken segments. Said deviations are logged and categorized to determine the areas in which said learner's pronunciation does not align with standard English pronunciation, providing an objective basis for generating corrective feedback.
In an embodiment, generating corrective feedback involves creating a response based on said identified deviations to guide said learner in modulating said pronunciation to align with standard English phonetic pronunciation. Said corrective feedback is tailored to the specific deviations detected in said learner's pronunciation and may provide detailed instructions on adjustments needed for articulation, such as changes in tongue positioning, lip movement, or voice modulation. Said feedback can also address suprasegmental elements, such as correcting stress or rhythm patterns in multi-syllable words or sentences. Said corrective feedback is generated in a form that is readily understandable by said learner, using both linguistic descriptions and acoustic examples, allowing said learner to hear or visualize how said correct pronunciation differs from said learner's original input.
In an embodiment, providing said corrective feedback to said learner through an auditory or visual medium involves delivering said feedback in a manner that facilitates understanding and real-time practice. Said auditory medium may include spoken instructions that are replayed to allow said learner to listen and repeat while comparing said pronunciation to a correct reference. Alternatively, said visual medium may provide graphical displays such as waveforms, mouth diagrams, or spectrograms that visually illustrate differences between said learner's pronunciation and said standard pronunciation. Said corrective feedback may be provided immediately during a pronunciation session to allow real-time correction, or it may be recorded for subsequent practice, depending on said learner's preferences and learning pace. Said provision of feedback aims to create an interactive learning environment where said learner is continuously guided and prompted to modify said pronunciation until said deviations are minimized.
In an embodiment, said pre-established phonetic pattern templates are derived from a database containing multiple variations of regional English accents. Said database may include numerous pronunciations from native speakers of different regions, representing diverse linguistic features and phonetic subtleties that vary from one accent to another. Said learner selects a preferred accent for practice, which allows customization of said phonetic pattern templates to meet said learner's goals. Said selection process may involve presenting said learner with different sample pronunciations from which an accent is chosen. Said database includes detailed acoustic data for each regional variation, capturing differences in articulation, intonation, and stress patterns. Said database is continuously updated to ensure coverage of contemporary and evolving speech patterns, thereby providing a relevant and effective basis for comparison. Said selected accent informs all subsequent stages of analysis, enabling said learner to focus on practicing pronunciation features that align with said specific accent preferences.
In an embodiment, said corrective feedback is delivered through a haptic device, providing tactile sensations that correspond to specific phonetic deviations. Said haptic device may include actuators that generate tactile signals, which vary based on the nature of said pronunciation deviation. For instance, a specific tactile pattern may be generated when a vowel is pronounced incorrectly, while another pattern may correspond to errors in consonant articulation. Said tactile feedback serves as a non-auditory means of providing immediate corrective signals to said learner, enhancing engagement by offering a different sensory input channel. Said haptic signals are designed to be intuitive, allowing said learner to quickly understand and respond to said feedback. Such tactile sensations can provide spatial cues, indicating, for example, where tongue placement or airflow adjustments may be needed to achieve correct pronunciation. Said haptic feedback can be used independently or in conjunction with other forms of feedback, such as visual or auditory.
In an embodiment, said analysis of said phonetic segments includes dynamic adjustment of said feedback based on said learner's progression and prior correction history. Said progression is tracked throughout multiple learning sessions, allowing said system to identify recurring patterns of errors and measure improvement over time. Based on said learner's history, said system dynamically adjusts the nature, intensity, and frequency of said corrective feedback to match said learner's current level of proficiency. For instance, as said learner shows improvement in specific phonetic areas, said feedback may become more subtle, focusing on finer nuances of pronunciation. Conversely, if repeated errors are identified, said feedback may become more explicit and detailed. Said adjustment is made to ensure that said learner is consistently challenged but not overwhelmed. Said dynamic adjustment also takes into account said learner's preferred learning style, which may be determined through observation of how said learner responds to different types of feedback.
In an embodiment, said pre-established phonetic pattern templates include stress and intonation patterns for multisyllabic words and phrases, wherein said corrective feedback addresses both segmental and suprasegmental features of pronunciation. Said segmental features involve individual phonemes, while said suprasegmental features include the rhythm, stress, and pitch variations that characterize natural speech. Said phonetic pattern templates incorporate data on how stress is typically distributed in multisyllabic words, as well as how intonation patterns rise and fall across phrases. Said corrective feedback generated during pronunciation practice therefore includes guidance on both proper articulation of individual phonemes and the appropriate application of stress and intonation. Said learner is prompted to adjust articulation and rhythmic flow to more accurately reflect standard speech patterns. By including both segmental and suprasegmental feedback, said learner is able to achieve more fluent and natural-sounding pronunciation.
In an embodiment, visual representation of said phonetic patterns using color-coded waveforms or spectrograms is provided, wherein each phoneme is distinguished by a unique color to aid pronunciation correction. Said visual representation allows said learner to see a visual depiction of said spoken phonetic segments in real-time or during playback. Said color-coded approach assigns a distinct color to each type of phoneme, enabling said learner to quickly identify specific sounds and their corresponding acoustic features. Said spectrograms illustrate the frequency and amplitude of said spoken segments, providing insight into aspects such as pitch, volume, and articulation over time. Said color-coding aids in distinguishing between different sounds, making it easier for said learner to identify discrepancies in pronunciation compared to said standard phonetic pattern templates. Such visual representation supports auditory feedback, allowing said learner to cross-reference between what is heard and what is seen, reinforcing learning.
In an embodiment, said corrective feedback is supplemented by interactive phonetic exercises, wherein said learner is prompted to mimic or produce phonetic sounds in isolation or within word contexts, followed by immediate assessment. Said exercises include interactive prompts where said learner is asked to reproduce specific phonemes, syllables, or full words, which are then analyzed by said system for accuracy. Said immediate assessment provides feedback that identifies any deviations in articulation or intonation, offering suggestions for improvement. Said interactive nature of said exercises is intended to provide a more engaging learning experience by involving said learner actively in said pronunciation process. Said exercises may also include repetition drills, contrastive exercises involving minimal pairs, and intonation practice to enhance both segmental and suprasegmental skills. Said assessment is carried out in real-time, and said learner is encouraged to make repeated attempts until pronunciation aligns with said standard template.
In an embodiment, said detecting of said phonetic segments involves the use of machine learning models trained to recognize specific phoneme categories and said deviations in both native and non-native speakers. Said machine learning models are trained using extensive datasets that include a wide range of phonetic examples from speakers of different linguistic backgrounds. Said models are capable of distinguishing between subtle differences in pronunciation that may arise due to varying accents or speech habits. Said machine learning models identify specific phonemes within said spoken input, categorizing said phonemes based on acoustic properties such as pitch, duration, and formant frequencies. Said models are also used to recognize deviations that occur due to incorrect articulation, stress, or intonation, enabling said system to generate appropriate corrective feedback. Said detection is thereby enhanced through the use of said machine learning models, which offer robust recognition across diverse speaker profiles.
In an embodiment, said corrective feedback includes real-time graphical visualizations of mouth and tongue movements, synchronized with auditory playback of corrected phonetic sounds. Said visualizations provide an illustrative guide to assist said learner in understanding how specific phonetic sounds are produced anatomically. Said graphical representations may include side views of said mouth cavity, showing tongue position, lip rounding, and airflow direction during pronunciation. Said visual feedback is synchronized with auditory playback, allowing said learner to see and hear how said correct pronunciation should be executed. Said visualizations are particularly helpful in cases where said learner struggles with specific articulation points, such as differentiating between voiced and voiceless sounds or achieving correct tongue placement for difficult consonants. By offering both auditory and visual information simultaneously, said corrective feedback provides a multi-sensory learning experience that enhances understanding of said physical actions required for proper pronunciation.
In an embodiment, storing a progress report of said learner's phonetic accuracy over time is included, wherein said progress report is accessible to both said learner and an instructor for evaluation and targeted learning strategies. Said progress report records said learner's performance across multiple practice sessions, capturing data related to phoneme accuracy, stress, and intonation over time. Said report may include visual graphs showing improvement trends, specific phonetic areas that require additional practice, and summaries of feedback provided during previous sessions. Said progress report allows said learner to track said learning journey and understand areas of consistent difficulty. Said instructor is also able to access said report, which can be used to develop targeted strategies for addressing said learner's specific challenges. Said stored data provides a valuable resource for both self-assessment and guided instruction, enhancing the overall effectiveness of said learning process by maintaining a continuous record of said learner's progress.
FIG. 2 illustrates decision based diagram of a method for teaching English pronunciation using phonetic patterns, in accordance with the embodiments of the present disclosure. The process begins by detecting phonetic segments within the spoken English input from a learner, which is then analyzed against pre-established phonetic pattern templates. A decision point follows, where the system checks for deviations from the standard phonetic patterns. If deviations are found, corrective feedback is generated to help the learner modulate these deviations to align with standard English pronunciation. This feedback is provided to the learner through either an auditory or visual medium. The learner is then prompted to modify their pronunciation, either in real-time or during a subsequent session. If no deviations are identified, the process ends without providing feedback. This flow ensures that the learner receives targeted feedback based on their specific pronunciation errors, fostering more effective learning and helping them improve their spoken English pronunciation by aligning their output with standard phonetic templates.
In an embodiment, detecting phonetic segments within spoken English input provides a technical benefit by enabling accurate breakdown of said spoken input into fundamental sound units. Such breakdown allows for targeted analysis of individual phonetic features, leading to precise identification of areas where said learner may be deviating from standard pronunciation patterns. Said detection also facilitates a structured approach to pronunciation improvement, focusing on small, manageable elements of speech rather than attempting to correct entire phrases at once. By segmenting spoken input into phonetic components, said method supports incremental learning and allows said learner to progressively improve articulation, resulting in a more effective overall learning experience.
In an embodiment, analyzing said phonetic segments against pre-established phonetic pattern templates derived from multiple regional accents provides the advantage of accommodating a wide range of linguistic preferences and needs. Said analysis enables said learner to focus on pronunciation that aligns with selected regional variations, thereby providing a more personalized learning experience. Such regional templates ensure that said learner can practice pronunciation that is contextually relevant to personal or professional requirements, enhancing engagement and applicability. Said analysis process also helps in providing context-specific feedback, making pronunciation correction more intuitive for said learner, as said reference templates directly relate to the chosen accent and encompass the expected phonetic characteristics of said regional speech patterns.
In an embodiment, providing corrective feedback through a haptic device delivers a unique tactile component to pronunciation learning. Said haptic feedback provides an additional sensory modality that complements traditional auditory and visual cues, making said learning experience more immersive. Such tactile sensations correspond to specific phonetic deviations, allowing said learner to physically sense pronunciation errors and aiding in internalizing said necessary adjustments. Said haptic signals can provide real-time reinforcement without requiring visual focus, making them particularly useful during auditory practice. Said multisensory approach is beneficial for learners who may need varied forms of reinforcement to effectively grasp proper articulation and modulation of phonetic elements.
In an embodiment, analyzing said phonetic segments with dynamic adjustment of said feedback based on said learner's progression provides an adaptive learning pathway. Said dynamic adjustment is achieved by tracking said learner's performance and tailoring said corrective responses to the learner's current proficiency. Said analysis takes into consideration said learner's previous errors, providing feedback that targets areas needing the most improvement while reducing redundancy. Said adjustment prevents said learner from becoming overwhelmed by unnecessary corrections, ensuring a consistent learning pace. Said method, therefore, supports gradual advancement through increasingly refined articulation guidance, focusing on said learner's unique progression path and promoting efficient improvement in pronunciation.
In an embodiment, including stress and intonation patterns within said phonetic pattern templates provides a more comprehensive approach to pronunciation correction. Such inclusion addresses suprasegmental features, including rhythm, pitch, and emphasis, which are crucial for achieving natural-sounding speech. Addressing segmental features alone may not provide sufficient context for proper speech modulation. Therefore, incorporating stress and intonation patterns allows said corrective feedback to encompass the full spectrum of pronunciation characteristics, providing said learner with guidance that extends beyond individual phoneme accuracy to encompass natural flow and expressiveness. Said method provides a holistic framework for improving overall communication skills by attending to both segmental and suprasegmental elements.
In an embodiment, providing visual representation of said phonetic patterns using color-coded waveforms or spectrograms introduces an intuitive means for understanding pronunciation characteristics. Said color-coding allows said learner to visually differentiate between phonemes, making identification of specific pronunciation features more straightforward. Such visual cues can help in highlighting areas where said learner deviates from standard pronunciation, providing a clear, visual basis for self-correction. Spectrograms also illustrate aspects such as frequency, intensity, and duration, allowing said learner to gain a deeper understanding of how sound properties contribute to proper pronunciation. Said visual representation acts as an effective supplementary tool to reinforce auditory and tactile feedback.
In an embodiment, supplementing said corrective feedback with interactive phonetic exercises encourages active participation from said learner, thereby enhancing engagement. Said exercises prompt said learner to mimic specific phonetic elements, either in isolation or within contextual word examples. Said interactivity supports immediate practice and reinforcement of learned concepts, helping to solidify pronunciation improvements. Such exercises also allow said learner to engage in focused practice sessions that directly address previously identified areas of difficulty, creating a loop of continuous assessment and correction. Said immediate assessment following each exercise provides actionable insights into pronunciation accuracy, further refining said learner's phonetic skills.
In an embodiment, using machine learning models to detect said phonetic segments facilitates robust recognition of pronunciation across diverse speaker profiles. Said machine learning models are trained with varied datasets, which include examples from both native and non-native speakers, providing the capability to recognize a wide range of pronunciation patterns. Such detection is capable of identifying subtle phonetic differences and articulatory features that manual detection may overlook, leading to a higher degree of accuracy in identifying deviations. Said models enable consistent and objective assessment of said learner's pronunciation, independent of human biases or variability, thus enhancing the reliability of said detection process and contributing to a more accurate feedback mechanism.
In an embodiment, providing real-time graphical visualizations of mouth and tongue movements, synchronized with auditory playback of corrected phonetic sounds, introduces an effective multisensory learning component. Said visualizations help said learner understand the physical articulatory actions required for accurate pronunciation. Such visual feedback is useful for learners who may struggle with conceptualizing how phonetic sounds are produced. By synchronizing said visual representations with auditory playback, said learner is able to correlate said visual cues with the corresponding sounds, gaining both anatomical and acoustic perspectives on pronunciation correction. Said real-time guidance makes said learning experience more interactive and directly applicable to immediate practice.
In an embodiment, storing a progress report of said learner's phonetic accuracy over time provides both said learner and an instructor with a detailed overview of said learning journey. Such a progress report includes metrics on phoneme accuracy, error frequency, and areas of improvement, serving as a valuable tool for evaluation. Said report allows said learner to self-assess improvements and identify ongoing challenges, promoting an independent learning mindset. Said instructor also benefits by having access to said learner's history, which can be used to formulate targeted learning interventions. Said systematic tracking of progress facilitates data-driven personalization of said learning experience, ensuring continuous, goal-oriented advancement.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams












I/We Claims


A method for teaching English pronunciation using phonetic patterns, comprising:
detecting phonetic segments within spoken English input from a learner;
analyzing said phonetic segments against pre-established phonetic pattern templates;
identifying deviations in said phonetic segments from said phonetic pattern templates based on a comparison of acoustic features;
generating corrective feedback, wherein said corrective feedback provides guidance for modulating said deviations to align with standard English phonetic pronunciation;
providing said corrective feedback to said learner through an auditory or visual medium, wherein said learner is prompted to modify said pronunciation in real-time or during a subsequent session.
The method of claim 1, wherein said pre-established phonetic pattern templates are derived from a database containing multiple variations of regional English accents, and wherein said learner selects a preferred accent for practice.
The method of claim 1, wherein said corrective feedback is delivered through a haptic device, providing tactile sensations corresponding to specific phonetic deviations.
The method of claim 1, wherein said analysis of said phonetic segments includes dynamic adjustment of said feedback based on the learner's progression and prior correction history.
The method of claim 1, wherein said pre-established phonetic pattern templates include stress and intonation patterns for multisyllabic words and phrases, and wherein said corrective feedback addresses both segmental and suprasegmental features of pronunciation.
The method of claim 1, further comprising visual representation of said phonetic patterns using color-coded waveforms or spectrograms, wherein each phoneme is distinguished by a unique color to aid pronunciation correction.
The method of claim 1, wherein said corrective feedback is supplemented by interactive phonetic exercises, wherein said learner is prompted to mimic or produce phonetic sounds in isolation or within word contexts, followed by immediate assessment.
The method of claim 1, wherein said detecting of said phonetic segments involves the use of machine learning models trained to recognize specific phoneme categories and said deviations in both native and non-native speakers.
The method of claim 1, wherein said corrective feedback includes real-time graphical visualizations of mouth and tongue movements, synchronized with auditory playback of corrected phonetic sounds.
The method of claim 1, further comprising storing a progress report of said learner's phonetic accuracy over time, wherein said progress report is accessible to both said learner and an instructor for evaluation and targeted learning strategies.




The present disclosure provides a method for teaching English pronunciation using phonetic patterns. Spoken English input from a learner is detected. Phonetic segments from said input are analyzed against pre-established phonetic pattern templates. Deviations in said phonetic segments from said templates are identified based on acoustic feature comparisons. Corrective feedback is generated, providing guidance for modulating said deviations to align with standard English phonetic pronunciation. Said corrective feedback is provided to said learner through an auditory or visual medium. Said learner is prompted to modify pronunciation either in real-time or during a subsequent session.

 , Claims:I/We Claims


A method for teaching English pronunciation using phonetic patterns, comprising:
detecting phonetic segments within spoken English input from a learner;
analyzing said phonetic segments against pre-established phonetic pattern templates;
identifying deviations in said phonetic segments from said phonetic pattern templates based on a comparison of acoustic features;
generating corrective feedback, wherein said corrective feedback provides guidance for modulating said deviations to align with standard English phonetic pronunciation;
providing said corrective feedback to said learner through an auditory or visual medium, wherein said learner is prompted to modify said pronunciation in real-time or during a subsequent session.
The method of claim 1, wherein said pre-established phonetic pattern templates are derived from a database containing multiple variations of regional English accents, and wherein said learner selects a preferred accent for practice.
The method of claim 1, wherein said corrective feedback is delivered through a haptic device, providing tactile sensations corresponding to specific phonetic deviations.
The method of claim 1, wherein said analysis of said phonetic segments includes dynamic adjustment of said feedback based on the learner's progression and prior correction history.
The method of claim 1, wherein said pre-established phonetic pattern templates include stress and intonation patterns for multisyllabic words and phrases, and wherein said corrective feedback addresses both segmental and suprasegmental features of pronunciation.
The method of claim 1, further comprising visual representation of said phonetic patterns using color-coded waveforms or spectrograms, wherein each phoneme is distinguished by a unique color to aid pronunciation correction.
The method of claim 1, wherein said corrective feedback is supplemented by interactive phonetic exercises, wherein said learner is prompted to mimic or produce phonetic sounds in isolation or within word contexts, followed by immediate assessment.
The method of claim 1, wherein said detecting of said phonetic segments involves the use of machine learning models trained to recognize specific phoneme categories and said deviations in both native and non-native speakers.
The method of claim 1, wherein said corrective feedback includes real-time graphical visualizations of mouth and tongue movements, synchronized with auditory playback of corrected phonetic sounds.
The method of claim 1, further comprising storing a progress report of said learner's phonetic accuracy over time, wherein said progress report is accessible to both said learner and an instructor for evaluation and targeted learning strategies.

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