This paper presents a speech recognition algorithm tailored for home appliance remote control applications. The system employs a two-module and two-level endpoint detection approach, significantly enhancing both recognition accuracy and robustness. It introduces a novel learning remote control system based on this technology. Speech recognition holds great potential in the home appliance industry, offering a more intuitive and user-friendly interface. This is especially beneficial for the elderly and disabled, as it removes barriers to interaction. Voice control is an effective way to improve the user experience of household appliances.
The paper uses voice-controlled remote controls as a case study 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. The first part handles analog-to-digital conversion, receiving voice signals and converting them into digital data for processing. At the output, it converts the digital signal back into an audio signal through a speaker. The second part is the speech recognition module, which analyzes the input signal and identifies commands, typically implemented using a Digital Signal Processor (DSP). The third part manages voice prompts and playback, enabling user interaction through voice feedback. The fourth part is the system control module, which translates the recognized command into a corresponding control signal to perform the desired function.
Currently, speech recognition in consumer electronics is often implemented using microcontrollers (MCUs) or DSPs. These systems typically support isolated word recognition with two common approaches: Hidden Markov Models (HMM) for non-specific person recognition and Dynamic Programming (DP) for specific person identification. While HMM offers stability without user training, it requires extensive pre-training data and struggles with dialect variations. On the other hand, DP-based systems are simpler and more flexible but suffer from lower robustness and performance degradation over time.
To address these challenges, the paper introduces a two-level endpoint detection method called FRED (Frame-based Real-time Endpoint Detection), which improves accuracy by analyzing energy and zero-crossing rate in the first stage, followed by frequency band analysis in the second. This method enhances adaptability to environmental noise and improves recognition performance. A comparison between FED and FRED algorithms shows that FRED significantly boosts recognition rates, making it ideal for use in home appliance remote controls.
Additionally, the paper discusses the Dynamic Time Warping (DTW) algorithm, which aligns speech feature sequences to account for variations in speaking rate. A dual-template strategy is proposed to enhance robustness, storing two templates per command for improved recognition accuracy. Testing results show that the dual-template approach significantly improves performance compared to single-template methods.
In summary, the embedded speech recognition system combines FRED for endpoint detection, MFCC features, and a dual-template DTW strategy. Through rigorous testing, the system demonstrates strong performance for specific person recognition, meeting the requirements of voice-controlled home appliances.
The design of a voice-controlled learning remote control is also presented. Unlike traditional remotes, this system allows users to define custom voice commands, reducing the need for memorizing complex button layouts. The hardware includes a DSP for speech processing, FLASH memory for storage, and a microcontroller for system control. The software flow ensures efficient operation, with sleep modes to conserve power when not in use.
Overall, the integration of speech recognition into home appliance remote controls represents a significant advancement in user interface design. With continued improvements in accuracy and robustness, this technology promises to revolutionize the way people interact with their devices.
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