Speech-to-Text System Context Recognition
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Solution Overview
Problem
Speech-to-text systems struggle to accurately recognize user utterances due to their inability to consider context, leading to incorrect textual representations and poor conversation quality in virtual assistant applications.
Innovation Solution
A machine-learning based Speech-to-Text system is configured to recognize user utterances by taking into account the latest generated phrases from virtual assistants, using neural networks with encoder-decoder architectures and attention mechanisms to improve context understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional Speech-to-Text system is used to recognize user utterances, then the system structure remains simple, but the recognition accuracy deteriorates due to inability to understand context
Solution Approach 1:
The patent combines the traditional Speech-to-Text recognition system with a context understanding module that processes conversation history and semantic relationships. This merging allows the system to maintain accurate transcription while adding contextual awareness, resolving the contradiction between recognition accuracy and system complexity.
Solution Approach 2:
The patent introduces an intermediary context processing layer between the audio input and the final text output. This intermediary module analyzes conversation history, identifies semantic relationships, and provides contextual information to enhance recognition accuracy without fundamentally redesigning the entire system architecture.
2Measurement precision
If the Speech-to-Text system processes only the current utterance without context, then the processing speed remains fast, but the semantic relation recognition deteriorates
Solution Approach 1:
The patent implements preliminary processing of conversation history and context information before the main recognition task. By pre-processing and storing contextual data in an accessible format, the system can quickly retrieve relevant context during recognition, improving semantic relation recognition without significant time penalty.
Solution Approach 2:
The patent segments the processing into distinct modules: context retrieval, semantic analysis, and recognition enhancement. This segmentation allows parallel processing of different aspects, reducing overall processing time while maintaining improved semantic relation recognition through dedicated context analysis.
3Adaptability or versatility
If the STT system expects full formal addresses, then the system configuration remains simple, but the adaptability to user speech patterns deteriorates
Solution Approach 1:
The patent implements a dynamic system that adapts to different user speech patterns and contexts. The context understanding module learns from conversation history and adjusts recognition expectations accordingly, allowing the system to handle both formal addresses and casual references like 'the doctor' or 'Neville Longbottom' without rigid configuration changes.
Data Source
AI summary
A method and a server for generating textual representations of a user utterance are provided. The method comprises: acquiring an audio signal being an audio representation of the user utterance, the user utterance being in response to a given machine-generated utterance previously outputted by the electronic device to the user; acquiring a machine-generated text string being a textual representation of the given machine-generated utterance; generating, using a Speech-to-Text (STT) model, an other text string based on the audio signal and the machine-generated text string, the other text string being a textual representation of the user utterance while taking into account the machine-generated text string as a context of the user utterance.