Phonetic Embedding Alignment for Real-Time Accent Conversion
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Solution Overview
Problem
Existing accent conversion systems face limitations such as non-differentiability, non-monotonicity, and instability in alignment algorithms, leading to inaccuracies and poor performance, especially with complex accents and limited training data.
Innovation Solution
The technology employs phonetic embedding vectors and a machine learning-based accent conversion neural network that maximizes cosine distance for alignment, using gradient-based optimization and loss functions to ensure stability and monotonicity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic time warping (DTW) algorithm is used for alignment, then temporal alignment of audio sequences is achieved, but the alignment algorithm becomes non-differentiable and cannot provide gradient information
Solution Approach 1:
The patent replaces the mechanical DTW alignment process with a neural network-based phonetic alignment system that uses differentiable operations. Instead of relying on non-differentiable DTW algorithms, the system uses neural networks to directly model phonetic alignments, enabling gradient-based optimization and end-to-end training of accent conversion models.
Solution Approach 2:
The patent changes the fundamental parameters of the alignment approach by transitioning from discrete DTW path finding to continuous neural network parameter optimization. This allows the system to learn alignment parameters through gradient descent, making the entire pipeline differentiable and trainable.
2Measurement precision
If DTW alignment is used, then audio alignment is performed, but the model can only learn from aligned data and not from original audio
Solution Approach 1:
The patent merges the alignment process with the learning process into a single unified neural network model. Instead of separating alignment and learning into two distinct steps, the system combines them so that the neural network learns directly from original audio data while performing alignment internally, enabling the model to leverage both aligned and unaligned data for training.
Solution Approach 2:
The patent introduces neural network layers as intermediaries between the input audio and the alignment process. These intermediary layers enable the system to learn representations from original audio while simultaneously achieving alignment, allowing the model to capture patterns from both sources without requiring separate training stages.
3Measurement precision
If DTW algorithm is used for alignment, then temporal alignment is achieved, but non-monotonicity and instability cause alignment errors
Solution Approach 1:
The patent implements feedback mechanisms through neural network loss functions that continuously monitor and adjust alignment quality. The system uses feedback from phonetic similarity metrics and loss functions to guide the alignment process, ensuring monotonic and stable alignments while correcting errors in real-time during training.
Solution Approach 2:
The patent makes the alignment system dynamic and adaptive by using neural networks that can adjust alignment parameters based on input characteristics. Instead of fixed DTW alignment, the system dynamically learns optimal alignments for different accent pairs and speakers, improving both accuracy and stability through continuous adaptation.
4Productivity
If existing accent conversion methods are used, then accent conversion is performed, but complex accents deviating from training data produce inaccurate results
Solution Approach 1:
The patent creates a universal accent conversion model that can handle diverse accents beyond the training data distribution. The neural network architecture is designed to learn general phonetic patterns and relationships, enabling it to accurately convert complex accents and speak to a broader range of accent types without requiring separate models for each accent pair.
Data Source
AI summary
The disclosed technology relates to methods, accent conversion systems, and non-transitory computer readable media for real-time accent conversion. In some examples, a set of phonetic embedding vectors is obtained for phonetic content representing a source accent and obtained from input audio data. A trained machine learning model is applied to the set of phonetic embedding vectors to generate a set of transformed phonetic embedding vectors corresponding to phonetic characteristics of speech data in a target accent. An alignment is determined by maximizing a cosine distance between the set of phonetic embedding vectors and the set of transformed phonetic embedding vectors. The speech data is then aligned to the phonetic content based on the determined alignment to generate output audio data representing the target accent. The disclosed technology transforms phonetic characteristics of a source accent to match the target accent more closely for efficient and seamless accent conversion in real-time applications.