Spoken Word Generation System for Speech Recognition
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Solution Overview
Problem
Existing speech recognition systems require preset word sets and acoustic models, which can lead to poor recognition results when users' preferred words or accents differ from the preset settings, limiting customization and user convenience.
Innovation Solution
A spoken word generation system that integrates a mode detection module to distinguish between speech recognition and training modes, allowing users to input specific sound events or control signals to train and add new words to the system, enabling personalized word models for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preset word sets and acoustic models are used for speech recognition, then the system can operate immediately, but recognition accuracy deteriorates when user preferences or regional accents differ from preset settings
Solution Approach 1:
The system performs preliminary actions by collecting speech data and training acoustic models in advance for multiple regions and accents. The server maintains pre-trained acoustic models for different regions (e.g., Mandarin for Taiwan, Ho-Lo for Hainan) so that when a user selects their region, the system can immediately use the appropriate pre-trained model without requiring real-time training, thus achieving both quick operation and high recognition accuracy for regional accents.
Solution Approach 2:
The system changes parameters by allowing users to select different region parameters, which triggers the system to load corresponding acoustic models and word sets optimized for that region. The server dynamically adjusts the acoustic model parameters based on the selected region, enabling the system to adapt to different accents and language preferences while maintaining high recognition accuracy.
2Reliability
If extensive customization and data collection are performed for different regions, then recognition accuracy for regional accents improves, but development time and complexity increase
Solution Approach 1:
The system achieves universality by creating a multi-functional speech recognition platform that serves multiple regions and accents through a single unified system. The server maintains a database of acoustic models for different regions (Taiwan, Hainan, etc.) and can dynamically select and switch between them based on user selection. This allows one system to perform multiple regional recognition functions without requiring separate customized systems for each region, thereby reducing overall complexity while maintaining high regional accuracy.
Solution Approach 2:
The server acts as an intermediary between the user's speech input and the recognition engine. It receives the selected region parameter, automatically retrieves the appropriate pre-trained acoustic model and word set from its database, and configures the recognition system accordingly. This intermediary layer handles the complex localization logic centrally, shielding the client devices from complexity while enabling accurate regional recognition.
3Adaptability or versatility
If the system requires preset word sets and acoustic models before operation, then the system structure remains simple, but the system cannot adapt to user preferences or regional variations
Solution Approach 1:
The system adds another dimension by introducing a server-based configuration layer that operates separately from the client devices. Instead of making each device complex and self-configurable, the system moves the adaptation logic to the server dimension. The server handles region selection, model retrieval, and system configuration, allowing client devices to remain simple while achieving high adaptability to user preferences and regional variations through the server's centralized control.
Data Source
AI summary
In a spoken word generation system for speech recognition, at least one input device receives a plurality of input signals at least including at least one sound signal; a mode detection module detects the plurality of input signals; when a specific sound event is detected in the at least one sound signal or at least one control signal is included in the plurality of input signals, a speech training mode is outputted; when no specific sound event is detected in the at least one sound signal and no control signal is included in the plurality of input signals, a speech recognition mode is outputted; a speech training module receives the speech training mode and performs a training process on the audio segment and outputs a training result; and a speech recognition module receives the speech recognition mode, and performs a speech recognition process and outputs a recognition result.