Dynamic Multilingual Speech Recognition via User Pattern Monitoring
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Solution Overview
Problem
Existing translation systems are not suited for real-time translation using audio input due to difficulties in recognizing languages on the fly, as they require pre-configurations and cannot dynamically determine the target language based on user preferences or contexts.
Innovation Solution
A method and computer program product that monitor multilingual switches to generate a service profile for users, determining a priority order for languages in a voice input stream, allowing for real-time translation without pre-configuration, by using machine learning to identify and adapt to language changes in audio inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing translation systems use pre-configurations to supply language information, then language recognition can be performed, but the system cannot adapt to real-time multilingual switches and user preferences
Solution Approach 1:
The patent implements dynamic language priority determination by continuously monitoring multilingual switches performed by users and updating service profiles in real-time. The system transitions from static pre-configurations to dynamic adaptation by detecting language switch patterns and adjusting translation priorities based on observed user behavior, enabling the system to adapt to changing linguistic contexts without manual reconfiguration.
Solution Approach 2:
The system performs self-service by automatically generating service profiles through machine learning algorithms that analyze monitored multilingual switch patterns. The system autonomously determines language priorities and updates its configuration without requiring external intervention or pre-programming, allowing it to adapt to user preferences and contextual factors independently.
2Measurement precision
If the system monitors multilingual switches to generate service profiles, then real-time language recognition improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by continuously monitoring and storing multilingual switch patterns in service profiles during normal operation. This ongoing data collection and pattern recognition prepares the system in advance, so when a translation request occurs, the language priority can be quickly determined from pre-analyzed patterns rather than analyzing raw data from scratch at the moment of translation.
Solution Approach 2:
The patent replaces mechanical analysis of language patterns with machine learning algorithms that automatically identify multilingual switch patterns and generate service profiles. This substitution of automated ML-based pattern recognition for manual or rule-based analysis improves both accuracy and efficiency, reducing the computational burden during actual translation operations.
3Ease of operation
If the system requires pre-configurations for language translation, then the translation process is straightforward, but the system cannot determine target language dynamically based on user preferences
Solution Approach 1:
The system determines target language dynamically through self-service mechanisms where the service profile, generated from monitored user behavior, automatically provides language priority information. This eliminates the need for manual pre-configuration of target languages while maintaining operational simplicity, as the system autonomously selects the appropriate target language based on learned user preferences and contextual factors.
Data Source
AI summary
A method, computer program product, and a system where a processor(s), monitors multilingual switches performed on a client on behalf of a given user. Based on the monitoring, the processor(s) identifies switch patterns of the given user to generate a service profile for the user of machine learned multilingual switch patterns for the given user. The processor(s) determines a priority order for languages comprising the voice input streams, for the given user. The processor(s) obtains a new translation request initiated by the client, on behalf of the given user and applies the priority order to identify one or more languages spoken in a voice input stream of the new translation request. The processor(s) transmits indicators of the identified one or more languages to the client, where upon receiving the indicators, the client translates the voice input stream from the identified one or more languages to one or more target languages.