This paper introduces a speech recognition algorithm tailored for home appliance remote control applications. The algorithm employs a two-module and two-level endpoint detection approach, significantly enhancing recognition accuracy and system robustness. It also presents a new type of learning remote control enabled by this technology. Speech recognition holds great potential in the home appliance industry, offering a more natural and user-friendly interface that benefits all users, especially the elderly and disabled. By integrating voice control, the user experience of household devices can be greatly improved.
The paper uses a voice-controlled remote as an example to demonstrate how speech recognition is applied in home appliances. The structure of the embedded speech recognition system suitable for home appliances is illustrated in Figure 1. It consists of four main components: analog-to-digital conversion, speech recognition, voice prompting and playback, and system control. The first part handles the conversion of audio signals into digital data for processing. The second part analyzes and recognizes voice commands using a Digital Signal Processor (DSP). The third part manages voice prompts and playback, facilitating human-machine interaction. The fourth part translates the recognized speech into control signals to perform specific actions. The integration of speech recognition with system control is essential for achieving seamless voice-activated interactions.
Currently, speech recognition in consumer electronics is often implemented using microcontrollers (MCUs) or DSPs. These systems typically focus on isolated word recognition, using either Hidden Markov Models (HMM) for non-specific speaker recognition or Dynamic Programming (DP) for specific speaker identification. While HMM offers stability without training, it requires large datasets and struggles with dialect variations. DP, on the other hand, is simpler and allows for user-defined commands but suffers from lower robustness and performance degradation over time.
To address these issues, a two-level endpoint detection method called FRED (Frame-based Real-time Endpoint Detection) has been proposed. This method improves the accuracy of detecting speech endpoints, which is crucial for isolated word recognition. Comparisons between FRED and the traditional FED algorithm show that FRED significantly enhances recognition rates, especially for specific speakers.
In addition, the paper discusses the use of the Dynamic Time Warping (DTW) algorithm for template matching. A dual-template strategy is introduced to improve recognition performance and robustness. Tests show that using two templates results in higher accuracy compared to a single template, making it a practical solution for voice-controlled remote systems.
The design of a voice-controlled remote includes a signal processing module and a system control module. The DSP plays a central role in speech recognition, while the microcontroller manages system operations and infrared communication. The system is designed to conserve power by entering sleep mode when not in use, ensuring long battery life.
Overall, the combination of advanced endpoint detection algorithms, efficient feature extraction, and dual-template strategies makes this system highly effective for voice control in home appliances. The technology has already been successfully implemented in learning remotes, demonstrating its potential for widespread adoption in the smart home market.
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