Bilingual Speech Recognition for Seamless Language Learning Dialog
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Solution Overview
Problem
Existing spoken language understanding systems struggle to seamlessly integrate voice-based language learning by requiring users to switch between languages for commands and learning inputs, leading to a suboptimal user experience.
Innovation Solution
A system that utilizes multiple ASR and phoneme recognition components configured for different languages, allowing users to provide commands in one language and learning inputs in another without additional input switching, by selecting the appropriate components based on device and learning language settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single ASR component configured for the device language is used, then the system can accurately process commands in the user's native language, but it cannot process learning inputs in the target language without requiring users to switch languages manually
Solution Approach 1:
The speech recognition system is enhanced to perform multiple language processing functions simultaneously. The ASR component is configured to handle both the device language (for commands) and the learning language (for pronunciation practice), allowing the system to serve multiple purposes without requiring users to switch between different systems or manually change configurations.
Solution Approach 2:
The system dynamically selects and switches between different ASR components based on the language context. When processing commands, it uses the device language ASR component; when processing learning inputs, it switches to the learning language ASR component. This dynamic adaptation allows seamless multilingual processing without user intervention.
2Adaptability or versatility
If multiple ASR components for different languages are integrated, then the system can process both commands and learning inputs in different languages, but the system complexity increases
Solution Approach 1:
The speech recognition system is divided into separate ASR components, each specialized for a specific language. Instead of creating one complex multilingual ASR system, the patent segments the functionality into multiple language-specific components that can be independently managed and selected based on the current processing needs.
Solution Approach 2:
An intermediary language detection and routing mechanism is introduced to manage the multiple ASR components. This intermediary automatically detects the language of the input and routes it to the appropriate ASR component, simplifying the overall system architecture by providing a clear separation between language detection and speech recognition functions.
3Measurement precision
If language switching is required for commands and learning inputs, then each language can be processed with optimal recognition accuracy, but the user experience deteriorates due to the need to switch languages
Solution Approach 1:
The system performs preliminary configuration of multiple language-specific ASR components before the user needs them. The language detection and component selection happen automatically and in advance, so when the user provides input in either language, the appropriate component is already ready to process it with optimal accuracy without requiring the user to manually switch languages.
Data Source
AI summary
Techniques for enabling voice-based language learning are described. A system of the present disclosure supports receipt of speech inputs in a user's native language and a language the user is learning. The system runs an automatic speech recognition (ASR) component configured for transcribing speech in the native language (e.g., English) and runs a phoneme recognition component configured for recognizing phonemes of the learning language (e.g., Spanish). The system can respond to commands provided in the native language (e.g., “Stop lesson”, “Repeat lesson”, etc.) and provide pronunciation feedback with respect to spoken inputs in the learning language.