Context-Aware Speech Recognition Prompts for Non-Native Accents
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Solution Overview
Problem
Existing automatic speech recognition systems struggle to accurately recognize non-native accented speech, leading to inaccurate transcriptions that hinder language learning by providing misleading feedback on grammar, pronunciation, and usage.
Innovation Solution
A context-aware speech recognition system that generates initial prompts with requested responses and ideal answers, conditioning a speech model to improve accuracy by leveraging the ideal answers as context, especially for non-native speakers, using a multimodal large language model to handle diverse speech patterns and accents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ASR systems are used for non-native speech recognition, then the system structure remains simple, but the recognition accuracy deteriorates due to inability to handle non-native accents and variability
Solution Approach 1:
The system performs preliminary actions by generating context prompts that include ideal answers before the actual speech recognition occurs. These prompts prepare the speech model with expected linguistic patterns and contexts, enabling more accurate recognition of non-native speech without requiring complex real-time processing during the actual recognition task.
Solution Approach 2:
The patent introduces context prompts as intermediary elements between the non-native speaker and the speech recognition system. These prompts act as a mediator that bridges the gap by providing the speech model with contextual information about expected responses, allowing accurate recognition without directly processing the complex variability of non-native accents in real-time.
2Reliability
If traditional ASR systems process speech without context, then the processing speed is fast, but the feedback quality for language learning deteriorates due to inaccurate transcriptions
Solution Approach 1:
The system generates context prompts with ideal answers in advance before processing the actual speech input. This preliminary preparation provides the speech model with the contextual framework needed for accurate recognition, ensuring reliable feedback for language learning while maintaining efficient processing by avoiding complex real-time analysis during the actual recognition task.
3Measurement precision
If the speech model is conditioned on ideal answers, then the recognition accuracy for non-native speakers improves, but the system complexity increases
Solution Approach 1:
The ideal answers are prepared in advance as part of the context prompt before the actual speech recognition occurs. This preliminary preparation allows the speech model to be conditioned on expected linguistic patterns without requiring complex adaptive processing during real-time recognition, thereby improving accuracy for non-native speakers while controlling model complexity.
Data Source
AI summary
A method includes generating an initial prompt including a requested response and an ideal answer. The requested response is configured to elicit a user to speak a respective utterance in a first language. The ideal answer represents how to correctly respond to the requested response. The method includes transmitting the requested response to a user device associated with the user. The user includes a native speaker of a second language different than the first language. After transmitting the initial prompt to the user device, the method includes receiving the audio data associated with the respective utterance in the first language spoken by the user. The method includes conditioning a speech model on the initial prompt. The method includes generating, using the conditioned speech model, a speech recognition result based on the audio data.