Parallel Multilingual Voice Recognition With Intent Arbitration
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Solution Overview
Problem
Existing voice-enabled devices struggle with universal language recognition, requiring lengthy setup for individual users or lacking means for language selection, and existing voice recognition models require extensive training for multiple languages and dialects.
Innovation Solution
A multilingual speech recognition system with parallel language models that analyze audio streams in real-time, using a ring buffer and arbitrator to select the most likely user intent based on confidence levels and selection frequencies, allowing devices to respond to voice commands in multiple languages without user input for language selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If voice recognition models are trained for each particular user for a specific set of commands, then language-agnostic recognition is achieved, but setup time becomes lengthy and cumbersome for each individual user
Solution Approach 1:
The system performs preliminary action by pre-training language models on multiple languages and commands before actual use. The arbitrator is pre-configured with rules for selecting between language models. This eliminates the need for lengthy setup procedures for each user while maintaining language-agnostic recognition capability, as the system can immediately switch between pre-trained language models based on the audio input.
2Adaptability or versatility
If voice/speech recognition models are trained to receive and identify directions spoken in a particular language, then multi-language operation is enabled, but the user must initially select an operating language which requires user input device capability
Solution Approach 1:
The system implements self-service by enabling automatic language detection and model selection without requiring user input. The arbitrator automatically selects the appropriate language model based on the audio stream analysis, making the system self-sufficient in determining the operating language. This eliminates the need for users to manually select a language through input devices.
Solution Approach 2:
The arbitrator serves as an intermediary component that mediates between the audio input and the language model selection. It receives audio streams, analyzes them, and automatically selects the appropriate language model without requiring direct user intervention. This intermediary mechanism enables multi-language operation while maintaining ease of operation by eliminating manual language selection.
3Adaptability or versatility
If voice recognition models are trained on a limited set of commands that can be pronounced in different languages, then language-agnostic recognition is achieved, but extensive training is required to recognize commands as spoken by persons of varying age and gender or in different languages and dialects
Solution Approach 1:
The system applies segmentation by dividing the voice recognition task into separate language-specific models rather than training a single model for all languages and dialects. Each language model is trained on commands pronounced in its specific language, allowing the system to handle linguistic variations without requiring extensive training of a single monolithic model. The arbitrator manages these segmented models to provide language-agnostic recognition.
4Productivity
If multiple language models operate in parallel to analyze audio streams, then real-time multi-language recognition is achieved, but system complexity increases
Solution Approach 1:
The system merges multiple language models into a unified architecture where parallel language models share common resources such as the audio stream input and the arbitrator output. This combining approach enables real-time multi-language recognition while managing system complexity through shared components and coordinated operation under the arbitrator's control.
Data Source
AI summary
A system and method for parallel multi-lingual speech recognition captures spoken instructions from a user as an audio stream concurrently fed to a set of language models, each language model trained to a set of words, phonemes, and/or pronunciations in a particular language. Operating in parallel, each language model attempts to detect an intent of the spoken instructions by correlating the audio stream to a sequence of words and/or phonemes with sufficient confidence. When a language model detects a candidate intent, the candidate intent is stored to an intent buffer. An arbitrator reviews the intent buffers for each language model and attempts to select or infer from the set of candidates a final intent of the spoken instructions best matching the user's intent. For example, if a clear selection cannot be made, the final intent may be inferred based on, e.g., highest confidence level or highest frequency of selection).