Noisy Channel Generative Model for Joint Sequence Learning
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Solution Overview
Problem
Existing machine learning approaches struggle to effectively learn the joint or conditional distribution of two sequences, such as text and speech, especially when limited or no paired data is available.
Innovation Solution
A computer-implemented method and system for learning a noisy channel generative model using a variational posterior model and KL encoder loss function, enabling the conversion of data from one sequence domain to another even with limited paired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a direct discriminative approach is used to model the conditional distribution of text given speech, then the modeling power can be focused solely on the task of interest, but learning from untranscribed speech audio becomes fundamentally impossible
Solution Approach 1:
The patent segments the learning task into two distinct phases: (1) pre-training the encoder model on unpaired speech audio to learn meaningful representations, and (2) fine-tuning the decoder model on paired data to learn the conditional distribution. This segmentation allows the system to leverage both unpaired and paired data effectively, resolving the contradiction between direct modeling accuracy and adaptability to unpaired data.
Solution Approach 2:
The patent applies preliminary action by pre-training the encoder model on untranscribed speech audio before the main training task. This preliminary step enables the model to learn robust speech representations from abundant unpaired data, which then serves as a foundation for the subsequent conditional distribution learning task with limited paired data.
2Adaptability or versatility
If a generative approach is used to model the joint distribution, then learning from untranscribed speech audio becomes possible, but the model becomes extremely sensitive to faulty assumptions in the speech audio model
Solution Approach 1:
The patent extracts the speech audio modeling task from the joint distribution modeling task by using a pre-trained encoder model. This extraction allows the system to leverage the encoder's learned representations from unpaired data without requiring the joint distribution model to make assumptions about the speech audio generation process, thereby reducing sensitivity to faulty assumptions while maintaining the ability to learn from untranscribed audio.
Solution Approach 2:
The patent introduces an intermediary encoder model that acts as a bridge between untranscribed speech audio and the conditional distribution modeling task. This intermediary component processes the speech audio and provides refined representations to the decoder, isolating the joint distribution model from direct exposure to speech audio modeling assumptions and reducing its sensitivity to such assumptions.
3Quantity of substance
If traditional generative modeling is used with limited paired data, then the model may overfit or fail to learn meaningful joint distributions, but increasing model complexity to capture relationships requires more data
Solution Approach 1:
The patent changes the dimensionality of the learning problem by moving from direct joint distribution modeling with limited paired data to a two-stage approach that first learns from the high-dimensional unpaired speech audio space and then projects this knowledge into the paired data space. This dimensional transformation allows effective learning despite the scarcity of paired data.
Solution Approach 2:
The patent changes the parameters of the learning process by using different objective functions for the two stages: the encoder is trained to maximize reconstruction likelihood on unpaired data, while the decoder is trained to model the conditional distribution on paired data. This parameter change enables the system to learn meaningful representations and relationships even with limited paired data by leveraging the broader unpaired data corpus.
Data Source
AI summary
Provided is a noisy channel generative model of two sequences, for example text and speech, which enables uncovering the associations between the two modalities when limited paired data is available. To address the intractability of the exact model under a realistic data set-up, example aspects of the present disclosure include a variational inference approximation. To train this variational model with categorical data, a KL encoder loss approach is proposed which has connections to the wake-sleep algorithm.