Speech-Text Representation Alignment Without Sampling for ASR Pre-Training
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Solution Overview
Problem
Deep learning-based automatic speech recognition (ASR) models tend to overfit training data, leading to difficulties in generalizing unseen data, especially when the training data is not extensive, and current methods for aligning speech and text modalities in training data result in misalignments.
Innovation Solution
A training process that pre-trains an audio encoder to jointly learn shared representations of speech and text by generating probability distributions over possible speech recognition hypotheses and determining a consistency loss based on these distributions, without requiring sampling or duration modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning-based ASR models are trained on limited training data, then model complexity can be kept manageable, but the models tend to overfit and fail to generalize to unseen data
Solution Approach 1:
The patent applies pre-training on extensive unlabeled speech data before fine-tuning on labeled data. This preliminary action allows the model to learn robust speech representations and temporal dynamics from large amounts of data, improving generalization ability while reducing overfitting risk during subsequent training on limited labeled datasets
Solution Approach 2:
The training process is segmented into distinct phases: pre-training on unlabeled data, then fine-tuning on labeled data. This segmentation allows the model to first learn general speech patterns without overfitting, then adapt to specific transcription tasks, thereby improving overall reliability and generalization
2Manufacturing precision
If speech and text modalities are aligned using traditional sampling methods, then training can be performed, but misalignments occur between speech and text representations
Solution Approach 1:
The patent introduces an intermediary alignment mechanism that operates in the latent space of the pre-trained model. This intermediary layer learns to map speech representations to text representations without requiring explicit sampling or duration modeling, thereby achieving accurate alignment while simplifying the overall system complexity
Solution Approach 2:
The patent replaces traditional mechanical alignment methods (sampling, duration modeling) with a learned neural network-based alignment mechanism. This substitution eliminates the need for complex sampling procedures and duration predictions, improving alignment accuracy while reducing computational complexity
3Adaptability or versatility
If extensive labeled human speech data is collected to improve model accuracy, then generalization improves, but data collection costs and time increase significantly
Solution Approach 1:
The patent performs preliminary pre-training on large amounts of unlabeled speech data that can be collected automatically without human annotation. This preliminary action extracts maximum value from easily obtainable data, improving model accuracy while minimizing the time and cost associated with collecting extensive labeled data
4Quantity of substance
If synthesized speech is used to increase training data volume, then data volume increases, but the quality and realism of training data may decrease
Solution Approach 1:
The patent leverages the pre-trained model's own capabilities to process and learn from unlabeled speech data. The model serves itself by extracting useful patterns from raw speech without requiring external synthesis, thereby maintaining high data quality while increasing training volume through efficient utilization of authentic human speech
Data Source
AI summary
A method includes receiving transcribed speech utterances, and for each respective transcribed speech utterance: generating, using a text encoder, a corresponding encoded textual representation of a corresponding transcription; generating a first probability distribution over possible speech recognition hypotheses for the corresponding encoded textual representation; generating, using a speech encoder, a corresponding encoded audio representation of the respective transcribed non-synthetic speech utterance; generating a second probability distribution over possible speech recognition hypotheses for the corresponding encoded audio representation, each possible speech recognition hypothesis of the second probability distribution comprising at least one blank output token; and determining a consistency loss based on the first and second probability distributions and the at least one non-blank output token of each possible speech recognition hypothesis of the second probability distribution. The method also includes pre-training the audio encoder based on the consistency losses.