Zero-shot Speech Intent Classification via Semantic Contrastive Loss
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech intent classification systems require extensive data collection and annotation for new domains, leading to high costs and limited adaptability, as they rely on supervised fine-tuning and are less effective in handling domain mismatch between pre-training and fine-tuning phases.
Innovation Solution
A novel framework using a semantic similarity-aware contrastive loss and large language models for zero-shot intent classification, which leverages pre-trained text encoders to generate class embeddings for speech encoders, enabling efficient adaptation to new domains without extensive data collection or manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised fine-tuning is used to adapt models to new domains, then model performance on target domain data is improved, but data collection and annotation costs increase significantly
Solution Approach 1:
The model performs self-supervised learning by predicting missing tokens in augmented speech sequences without requiring external annotations. The system uses its own predictions and augmentations to generate training signals, eliminating the need for manually annotated target domain data while adapting to new domains through self-generated supervisory information
Solution Approach 2:
The model is pre-trained on large amounts of unannotated source domain speech data before deployment. This preliminary self-supervised pre-training equips the model with general speech understanding capabilities, allowing it to adapt to new domains with minimal or no additional annotation costs through subsequent fine-tuning or zero-shot transfer
2Measurement precision
If domain-specific fine-tuning is performed, then accuracy on in-domain data is improved, but adaptability to out-of-domain data deteriorates
Solution Approach 1:
The model learns universal speech representation patterns through self-supervised pre-training on diverse source domain data, enabling it to function effectively across multiple domains. The learned representations capture fundamental speech characteristics that transfer across domains, allowing the model to maintain both in-domain accuracy and out-of-domain adaptability without specialized fine-tuning for each domain
3Measurement precision
If traditional supervised learning is used, then intent classification accuracy is improved, but data collection time and cost increase
Solution Approach 1:
The system generates its own training data and labels through self-supervised mechanisms, using data augmentation and masked language modeling to create supervisory signals automatically. This eliminates the need for time-consuming manual data collection and annotation while maintaining competitive classification accuracy through self-generated training examples
Solution Approach 2:
The model creates augmented copies of existing speech data through various transformations (pitch shifting, time stretching, noise addition) to generate diverse training examples. These synthesized copies serve as additional training data without requiring new manual recordings or annotations, significantly reducing data collection time while providing sufficient training material for accurate intent classification
Data Source
AI summary
A method includes: receiving one or more training text sentences; generating one or more training vectors based on inputting the one or more training sentences input into a text encoder, the one or more training vectors corresponding to one or more operations that an electronic device is configured to perform; generating one or more speech vectors based on one or more speech utterances input into a speech encoder; generating a similarity matrix that compares each of the one or more training vectors with each of the one or more speech vectors; and updating at least one of the text encoder and the speech encoder based on the similarity matrix.