Speech Recognition With Early Intent Estimation Feedback
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Solution Overview
Problem
Existing social networking systems require users to learn specific verbal commands, and the accuracy of intent classification is limited by the quality of speech recognition, leading to frustrating and inefficient user interactions.
Innovation Solution
A speech recognition system is trained end-to-end with an intent classifier, processing raw speech input to generate initial character-based representations, allowing intent classification before complete transcription, and refining the transcription based on intent estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition is used with separate intent classification, then the system can process speech input, but the accuracy of intent classification is limited by the quality of speech recognition and users must learn specific commands
Solution Approach 1:
The patent merges speech recognition and intent classification into a unified end-to-end neural network model. The model simultaneously performs speech-to-text transcription and intent detection, eliminating the sequential dependency where intent classification relies on preliminary speech recognition output. This integration allows the system to achieve higher intent classification accuracy without requiring users to learn specific commands, as the model infers intent directly from speech patterns and context.
Solution Approach 2:
The end-to-end model incorporates feedback mechanisms where the intent classification output influences the speech recognition transcription process. The neural network adjusts its transcription based on contextual clues and intent predictions, creating a feedback loop that continuously refines both transcription accuracy and intent detection. This feedback mechanism enables the system to handle ambiguous speech inputs more effectively and reduces the need for users to use precise, pre-learned commands.
2Device complexity
If speech recognition is performed before intent classification, then the processing sequence is straightforward, but the intent classification accuracy is limited by transcription quality
Solution Approach 1:
The patent combines speech recognition and intent classification into a single end-to-end processing pipeline that operates simultaneously rather than sequentially. The unified neural network model processes speech input through both transcription and intent detection pathways in parallel, eliminating the bottleneck where intent classification waits for transcription completion. This merging of functions maintains processing simplicity while dramatically improving intent classification accuracy through joint optimization of both tasks.
Solution Approach 2:
The end-to-end model performs preliminary intent estimation during the speech recognition process itself, rather than waiting for complete transcription. The neural network extracts intent-related features from raw speech signals and intermediate transcription representations, enabling intent classification to proceed in advance. This preliminary action allows the system to make intent predictions based on partial information, improving accuracy while maintaining straightforward processing architecture.
3Ease of manufacture
If separate speech recognition and intent classification models are used, then each model can be optimized independently, but the overall system efficiency and accuracy are reduced
Solution Approach 1:
The patent integrates speech recognition and intent classification into a single end-to-end neural network model that trains and processes data as one unified system. This merging eliminates the inefficiencies of separate model optimization, as the unified model can leverage shared computational resources, unified training data, and coordinated parameter updates. The end-to-end architecture improves overall system processing efficiency by reducing the number of sequential processing stages and enabling joint optimization of transcription and intent detection parameters.
Solution Approach 2:
The end-to-end neural network model serves multiple functions simultaneously: it performs speech-to-text transcription, intent classification, and contextual understanding within a single unified architecture. This multi-functionality eliminates the need for separate specialized models, improving system processing efficiency by reducing redundant computations and data transfers. The universal model handles all speech processing tasks through a single optimized network structure, enhancing overall productivity while maintaining the ability to perform each function accurately.
Data Source
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AI summary
Systems and methods are provided for performing speech to intent classification. The systems and methods perform operations comprising: receiving an audio file comprising speech input; processing, by a speech recognition engine, the audio file comprising the speech input to generate an initial character-based representation of the speech input; processing, by an intent classifier, the initial character-based representation of the speech input to generate an estimated intent of the speech input; and generating, by the speech recognition engine, a textual representation of the speech input based on the estimated intent of the speech input.