Phoneme-Guided Speech Pre-Training for Interpretable Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing self-supervised learning models for speech processing are not optimized for downstream tasks and lack performance in applications such as automatic speech recognition and text-to-speech due to unspecific pre-training methods.
Innovation Solution
A speech pre-training method that utilizes phoneme information to cluster speech features, enabling more controllable and interpretable clustering, which is then used to train a network model for improved performance in downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If self-supervised learning models are pre-trained on large amounts of unlabeled data in an application-agnostic manner, then the models can be broadly applicable to multiple speech tasks, but the performance in downstream speech tasks is insufficient
Solution Approach 1:
The speech data is segmented into phoneme-level units, and clustering is performed at the phoneme level rather than on raw speech frames. This segmentation allows the model to learn phoneme-specific features that are then applicable across multiple downstream tasks, resolving the contradiction between broad applicability and task-specific performance
Solution Approach 2:
The method performs preliminary phoneme-aligned clustering on unlabeled speech data before fine-tuning on downstream tasks. This preliminary action creates phoneme-level representations that prepare the model for better performance in subsequent downstream tasks while maintaining broad applicability
2Productivity
If clustering is performed without phoneme information, then the process is simpler and faster, but the clustering results lack interpretability and controllability
Solution Approach 1:
Phoneme information is introduced as an intermediary between raw speech data and clustering results. The phoneme alignments serve as a mediator that guides the clustering process, making the results both interpretable (through phoneme labels) and controllable (through phoneme-based initialization), while the clustering itself remains computationally efficient
3Adaptability or versatility
If the entire network is updated during fine-tuning on downstream tasks, then the model can adapt to specific tasks, but the training complexity and computational cost increase
Solution Approach 1:
The method extracts and freezes the phoneme-level cluster assignments as fixed representations during downstream task fine-tuning. By extracting these phoneme representations and treating them as fixed features rather than updating the entire network, the model achieves task-specific adaptation with reduced training complexity and computational cost
Data Source
AI summary
Described is speech pre-training, which includes acquiring a speech sample and phoneme data corresponding to the speech sample. Speech features of speech frames are extracted in the speech sample. Based on the speech features and the phoneme data, the speech sample is divided into at least one speech segment, where one speech segment corresponds to one phoneme. Based on speech features of speech frames in speech segments corresponding to a same phoneme, target features of phonemes are determined. The target features of the phonemes are used as initial clustering centers. Based on the initial clustering centers and to obtain corresponding clustering labels, the speech features of the speech frames in the speech sample are clustered. By using the corresponding clustering labels to obtain a speech pre-training model, a predetermined network model is trained.