Teacher-Student ML Model for Real-Time Speech Form Conversion
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Solution Overview
Problem
Existing technologies face challenges in converting speech audio from one form to another, such as accent conversion, in real-time while maintaining textual integrity and synchronicity, due to the need for extensive computing resources and the difficulty in synchronizing audio streams.
Innovation Solution
The development of a machine learning-based system that generates synchronized multi-form speech data for training speech form conversion models, allowing for real-time conversion by using a Teacher Model to produce parallel data and a Student Model for real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech audio is converted to text and then back to audio for form conversion, then conversion flexibility is improved, but temporal synchronization is lost and computing resources are excessive
Solution Approach 1:
The patent introduces an intermediate latent space representation that serves as a mediator between input and target audio forms. This latent space preserves temporal information while enabling flexible form conversion, avoiding the need to convert to text and back, thus maintaining synchronization while achieving conversion flexibility.
Solution Approach 2:
The patent replaces the mechanical text-based intermediate conversion process with a direct neural network-based audio-to-audio transformation through latent space. This substitution eliminates the temporal information loss inherent in text conversion while reducing computing resource requirements compared to traditional text-based approaches.
2Measurement precision
If large amounts of synchronized speech data are collected for training, then model accuracy is improved, but data collection difficulty increases
Solution Approach 1:
The patent performs preliminary action by training a Teacher Model offline with unlimited lookahead using large datasets, generating high-quality parallel training data in advance. This pre-computed parallel data is then used to train the Student Model for real-time conversion, eliminating the need to collect and synchronize large amounts of parallel data in real-time while maintaining high model accuracy.
Solution Approach 2:
The patent uses the Teacher Model to generate synthetic parallel training data that copies the quality and synchronization characteristics of real multi-form speech data. This copying approach provides accurate training data without requiring actual collection of large volumes of synchronized speech recordings, significantly reducing data collection difficulty.
3Measurement precision
If Teacher Model with unlimited lookahead is trained, then training accuracy is improved, but real-time processing capability is reduced
Solution Approach 1:
The patent segments the conversion system into two distinct models: a Teacher Model for offline training with unlimited lookahead to achieve high accuracy, and a Student Model for real-time processing with limited lookahead. This segmentation allows each model to be optimized for its specific function, resolving the contradiction between training accuracy and real-time processing capability.
Solution Approach 2:
The patent applies preliminary action by performing all high-accuracy training computations in advance using the Teacher Model with unlimited lookahead. The Student Model is then trained on pre-generated parallel data from the Teacher Model, enabling real-time conversion without requiring the Student Model to perform computationally intensive unlimited lookahead processing during actual use.
4Speed
If Student Model processes speech frame-by-frame in real-time, then processing speed is improved, but synchronization precision is reduced
Solution Approach 1:
The patent performs preliminary action by having the Teacher Model pre-compute accurate parallel data with precise temporal alignment during offline training. This pre-computed parallel data encodes the synchronization information that the Student Model can leverage during real-time processing, maintaining synchronization precision while enabling frame-by-frame processing speed.
Solution Approach 2:
The patent implements feedback by using the Teacher Model's pre-computed parallel data as training feedback for the Student Model. This feedback mechanism allows the Student Model to learn the precise temporal relationships and synchronization characteristics from the Teacher Model's training data, maintaining synchronization precision during real-time frame-by-frame processing.
Data Source
AI summary
Techniques are described for generating parallel data for real-time speech form conversion. In an embodiment, based at least in part on input speech data of an original form, a speech machine learning (ML) model generates parallel speech data. The parallel speech data includes the input speech data of the original form and temporally aligned output speech data of a target form different than the original form. Each frame of the input speech data temporally corresponds to the corresponding output speech frame of the target speech form and contains a same portion of the particular content. The techniques further include training a teacher machine learning model that is offline and is substantially larger than a student machine learning model for converting speech form. Transferring “knowledge” from the trained Teacher model for training the Production Student Model that performs the speech form conversion on an end-user computing device.