Combined Speech Recognition System for Low-Resource Vocal Interfaces
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Solution Overview
Problem
Conventional vocal user interface (VUI) systems, particularly those using deep neural networks, face challenges in low-resource scenarios due to the need for extensive training data, limited usability across languages, and high computational costs, making them unsuitable for personalized and language-independent applications, especially for users with speech disorders.
Innovation Solution
A combined system integrating a self-learning speech recognition system based on semantics with a speech-to-text system, utilizing a decision fusion module to automatically learn and improve recognition accuracy without requiring explicit user feedback, allowing for language-independent and personalized VUIs that can recognize new commands and phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network based ASR systems are used, then speech recognition accuracy is improved, but the requirement for large training datasets increases
Solution Approach 1:
The system performs preliminary action by pre-training the deep neural network on a large general speech corpus beforehand. This preliminary training establishes a strong baseline model that can then be adapted to specific users or languages with minimal additional data, thus resolving the contradiction between achieving high accuracy and requiring large training datasets for each application scenario.
Solution Approach 2:
The system changes parameters by transitioning from a static ASR model to a dynamic adaptation model that modifies its parameters based on user feedback and interaction patterns. This allows the system to maintain high recognition accuracy while requiring minimal training data from individual users, as the model parameters are continuously optimized during actual use rather than requiring extensive pre-training for each user.
2Adaptability or versatility
If conventional ASR systems are used, then speech-to-text conversion is achieved, but language independence and personalization are limited
Solution Approach 1:
The system implements self-service by automatically adapting to the user's language patterns, accent, and speech characteristics without requiring manual configuration. The ASR model continuously learns from user interactions and automatically adjusts its parameters, eliminating the need for complex setup procedures and making the system truly language-independent and personalized.
Solution Approach 2:
The system applies dynamics by making the ASR model adaptable and changeable over time. Rather than being fixed to specific languages or users, the model dynamically adjusts its parameters and characteristics based on the actual speech patterns it encounters, enabling it to serve multiple languages and users effectively without increasing configuration complexity.
3Reliability
If deep learning ASR systems are deployed, then recognition capability is enhanced, but computational cost increases
Solution Approach 1:
The system segments the speech recognition task into two parts: a complex deep neural network component for general speech understanding and a simpler adaptation component for user-specific adjustments. This segmentation allows the heavy computational lifting to be done once during pre-training, while ongoing use requires minimal computational resources, thus maintaining high recognition capability while reducing operational computational costs.
4Adaptability or versatility
If ASR systems with fixed vocabulary are used, then system simplicity is maintained, but the ability to recognize new commands and phrases is limited
Solution Approach 1:
The system enables self-service by automatically learning and incorporating new commands and phrases from user speech without requiring manual vocabulary updates. The deep neural network model inherently captures new speech patterns and concepts from the data it processes, allowing the system to expand its command recognition capability organically without increasing vocabulary management complexity.
Data Source
AI summary
The present disclosure relates to speech recognition systems and methods that enable personalized vocal user interfaces. More specifically, the present disclosure relates to combining a self-learning speech recognition system based on semantics with a speech-to-text system optionally integrated with a natural language processing system. The combined system has the advantage of automatically and continually training the semantics-based speech recognition system and increasing recognition accuracy.