Multilingual Wakeword Detection Using Segmented DSP Components
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Solution Overview
Problem
Current speech recognition systems face challenges in efficiently detecting wake words across multiple languages, particularly in environments where different languages are spoken, as they often require significant computational resources and struggle to recognize wake words with varying accents or pronunciations.
Innovation Solution
A multilingual wake word detection system that employs a small, low-power first detection component to identify the language of the wake word, activating a more accurate second component for further processing, utilizing digital signal processing and hidden Markov models to select language-specific models for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive speech recognition system is used to detect wake words in multiple languages, then the detection accuracy and language support are improved, but the computational resources and processing power requirements increase significantly
Solution Approach 1:
The wake word detection system is divided into multiple language-specific components, each specialized for detecting wake words in a particular language. This segmentation allows the system to process only the relevant language component when a wake word is detected, rather than processing all languages simultaneously, thereby reducing overall computational resource consumption while maintaining multilingual capability.
Solution Approach 2:
The system dynamically activates different language-specific wake word detection components based on the detected language. When a wake word is detected in a specific language, the system activates only the corresponding language-specific component for further processing, making the computational resources adaptable and flexible rather than continuously consuming resources for all languages.
2Measurement precision
If language-specific wake word detection models are used for each language, then the detection accuracy for each language is improved, but the system complexity increases
Solution Approach 1:
The system architecture is segmented into multiple independent language-specific wake word detection components, each optimized for its specific language. This segmentation allows each component to achieve high detection accuracy for its target language while maintaining a relatively simple individual structure, as each component only needs to handle one language rather than all languages.
Solution Approach 2:
The system employs a universal wake word detection framework that can accommodate multiple language-specific components. This universal framework provides common functionality for all language-specific models, such as audio processing and wake word identification, while allowing each language-specific component to specialize in its own language, thus achieving both high accuracy and manageable complexity.
3Device complexity
If a single comprehensive wake word detection model is used for all languages, then the device complexity is reduced, but the computational efficiency and resource utilization deteriorate
Solution Approach 1:
The system segments the wake word detection functionality into separate language-specific models rather than using a single comprehensive model. This segmentation enables the system to process only the relevant language data for each wake word detection event, improving computational efficiency by avoiding unnecessary processing of other languages while maintaining a relatively simple overall system architecture through modular design.
Solution Approach 2:
The system dynamically selects and activates the appropriate language-specific wake word detection model based on the detected language. This dynamic activation mechanism improves computational efficiency by ensuring that only the necessary language-specific models are processed, rather than continuously processing all languages with a single comprehensive model, while the modular architecture keeps the overall system complexity manageable.
Data Source
AI summary
A system and method performs multilingual wakeword detection by determining a language corresponding to the wakeword. A first wakeword-detection component, which may execute using a digital-signal processor, determines that audio data includes a representation of the wakeword and determines a language corresponding to the wakeword. A second, more accurate wakeword-detection component may then process the audio data using the language to confirm that it includes the representation of the wakeword. The audio data may then be sent to a remote system for further processing.