Accent Training System Using Deep Learning for Personalized Pronunciation
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Solution Overview
Problem
Current language instruction systems inadequately address the challenge of developing an appropriate accent when learning a new language, often relying on generalized reference models that are ineffective and unrealistic for users, leading to slow progress in pronunciation training.
Innovation Solution
A computer-implemented method using deep learning models to identify and prioritize improvable aspects of a user's accent, selecting focus phrases based on learnability scores, and converting user input into accent-corrected audio to provide personalized training, allowing users to practice in their own voice with the target accent applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generalized reference models are used for accent training, then the system can provide standardized pronunciation guidance, but the training effectiveness decreases for users with diverse baseline accents
Solution Approach 1:
The system segments the accent training process into multiple components: baseline accent identification, target accent analysis, phoneme-level improvement identification, and personalized phrase selection. This segmentation allows the system to handle diverse baseline accents effectively while maintaining reliable training outcomes through focused, incremental improvements on specific phonemes and sound combinations.
Solution Approach 2:
The system dynamically changes training parameters based on the user's baseline accent characteristics. It identifies specific phonemes and sound combinations that need improvement and selects training phrases containing those elements. The training content and difficulty are adjusted according to the user's performance history and learning progress, making the system adaptable to different accents while maintaining effective training.
2Stability of the object's composition
If standardized reference targets are used for accent imitation, then the system can provide consistent training material, but user progress slows due to unrealistic expectations
Solution Approach 1:
Instead of requiring uniform imitation of standardized reference targets across all phonemes, the system applies local quality by identifying specific phonemes and sound combinations where the user needs improvement. Training phrases are selected to contain these specific target phonemes, allowing users to focus on localized improvements rather than attempting to replicate entire phrases with perfect accent, thereby accelerating progress while maintaining consistent training material.
Solution Approach 2:
The system implements partial action by focusing training on specific phonemes and sound combinations rather than requiring complete accent replication. Users practice targeted elements of the target accent through selected phrases, making the training more manageable and progressive. This partial focus on critical improvement areas speeds up learning compared to attempting comprehensive accent imitation from the start.
3Measurement precision
If deep learning models are used to personalize training, then the system can identify specific improvable aspects, but the system complexity increases
Solution Approach 1:
The system uses deep learning models as intermediaries between the user's speech input and the training content selection. These models automatically analyze baseline accents, identify target phonemes for improvement, and select appropriate training phrases. While the underlying system complexity increases, the deep learning models act as intelligent mediators that automate the analysis and personalization process, reducing the need for manual configuration and providing precise identification of improvable aspects.
Data Source
AI summary
A computer assists in training a user to speak with a target accent by determining improvable aspects of diagnostic input of a user speaking diagnostic phrases with the target accent. The computer selects, a focus phrase characterized by at least one of said improvable aspects. The computer records performance input of the user attempting to say the focus phrase in the target accent. The computer converts the performance input into output having a baseline voice of the and the target accent applied. The computer presents the output and determines teachable aspects of revised performance input from the user replicating the output. The computer converts selected aspects of the revised performance input into augmented teaching output in the user's voice with the target accent applied. The computer presents the augmented teaching output to the user.