Multi-modal SLU System Audio Feature Integration
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Solution Overview
Problem
Spoken language understanding (SLU) systems face challenges in accurately identifying intents and slot labels from speech utterances due to errors in automatic speech recognition (ASR), which can propagate downstream, and struggle with slot labeling in end-to-end configurations.
Innovation Solution
A multi-modal SLU system that combines transcripts from ASR with audio feature vectors, capturing non-transcript specific characteristics like emotion and tone, to provide comprehensive input for language understanding, using synchronizers to align and concatenate these streams for improved prediction performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If ASR transcripts are used as input for language understanding, then the system can process speech utterances through a modular architecture, but errors in ASR outputs propagate downstream and reduce prediction accuracy
Solution Approach 1:
The patent merges ASR transcripts with audio feature vectors into a unified multi-modal input representation. The language understanding module processes both text-based transcript information and audio-based feature information simultaneously, allowing the system to leverage complementary strengths of both modalities while compensating for weaknesses in either modality alone.
Solution Approach 2:
The patent introduces audio feature vectors as an intermediary representation that bridges the gap between raw speech signals and text transcripts. These audio features serve as a complementary mediator that provides additional linguistic and paralinguistic information not captured by ASR, thereby reducing error propagation and improving prediction accuracy.
2Productivity
If end-to-end configuration is adopted for SLU, then slot labeling can be performed directly from speech utterances, but it becomes challenging or infeasible to perform slot labeling
Solution Approach 1:
The patent segments the speech processing task into distinct modalities: text-based transcript processing and audio-based feature processing. Each modality is handled by specialized processing paths within the language understanding module, allowing slot labeling to be performed effectively by integrating information from both segments rather than attempting a monolithic end-to-end approach.
Solution Approach 2:
The patent creates a multi-functional language understanding module that can process both transcript information and audio feature information through a unified architecture. This multi-functional design enables the system to perform various tasks including intent classification and slot labeling by leveraging complementary information from multiple input modalities.
Data Source
AI summary
A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.