End-to-End Spoken Language Understanding System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional spoken language understanding (SLU) systems require separate processing resources for speech recognition and natural language understanding components, leading to suboptimal performance due to resource allocation challenges and reliance on human-generated training data, with separate training criteria for each component and susceptibility to errors in real-world scenarios.
Innovation Solution
An end-to-end SLU system that processes audio features directly to infer semantic meaning using a multilayer bidirectional gated recurrent unit network, eliminating the need for intermediate text representation and allowing all processing resources to be devoted to a single component, thereby improving accuracy and reducing the need for human-generated training materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate ASR and NLU components are used in a pipeline architecture, then each component can be optimized independently, but the overall system performance deteriorates due to resource allocation challenges and error propagation
Solution Approach 1:
The patent merges the ASR and NLU components into a single unified neural network model that processes audio input directly to generate semantic output. This integration eliminates the interface between separate components, preventing error propagation and allowing the system to optimize the entire processing pipeline simultaneously rather than individually, thereby resolving the contradiction between component optimization and system performance.
Solution Approach 2:
The unified neural network model performs multiple functions within a single architecture: it conducts speech recognition, natural language understanding, and semantic classification simultaneously. This multi-functional approach allows the system to allocate resources efficiently across all processing tasks while maintaining consistent optimization goals, addressing both the ease of manufacture and reliability requirements.
2Adaptability or versatility
If multiple separate processing components are used, then each component can specialize in its task, but the device complexity increases and resource allocation becomes suboptimal
Solution Approach 1:
The patent combines multiple specialized processing functions into a single neural network architecture that handles audio processing, transcription, and semantic classification in one unified system. This merger reduces device complexity by eliminating multiple separate components and their interconnections, while the network's modular internal structure preserves task specialization capabilities through different layers and processing stages.
3Measurement precision
If human-generated training data is used for each component separately, then each component can be trained with task-specific data, but the amount of training data required increases and errors propagate through the pipeline
Solution Approach 1:
The patent merges the training processes of ASR and NLU into a single unified training framework where the neural network learns from audio-semantic pairs directly. This approach reduces the total training data volume by eliminating the need for intermediate transcript data and separate training corpora for each component. The unified model learns end-to-end mappings from audio to semantic meaning, maintaining training accuracy while reducing data requirements and preventing error propagation through the training pipeline.
Data Source
AI summary
Exemplary embodiments relate to improvements in spoken language understanding (SLU) systems. Conventionally, SLU systems include an automatic speech recognition (ASR) component configured to receive an input of audio data and to generate a textual representation of the audio data. Conventional SLU systems also include a natural language understanding (NLU) component configured to receive a text-based transcript and perform language-based tasks such as domain classification, intent determination, and slot-filling. However, these two components are typically trained separately based on different metrics. In real-world situations, errors in the ASR component propagate to the NLU component, which degrades the performance of the overall system. Exemplary embodiments described herein perform SLU in an end-to-end manner that infers semantic meaning directly from audio features without an intermediate text representation. This may allow for more a more accurate translation performed in a more resource-efficient manner (particularly in terms of processing resources).