To accurately capture the speaker's voice, the smart lecture table's voice recognition function relies first on the collaborative work of a multi-microphone array. These microphones are distributed on the surface of the podium at specific angles to form a directional sound collection area, just like building a "dedicated channel" for the speaker's voice. When the speaker stands in front of the stage and speaks, the array will prioritize capturing the sound from the front, while weakening the noise from the sides and the back. This spatial filtering ability allows the speaker's voice to dominate the sound collection stage, laying the foundation for subsequent recognition.
Advanced algorithm optimization is the core of improving recognition accuracy. The system will accumulate a large number of voice samples through deep learning, gradually become familiar with the pronunciation characteristics of different genders, ages, and accents, and even recognize the changes in the speaker's tone caused by emotional fluctuations. When the speaker speaks faster or pauses suddenly, the algorithm will automatically adjust the recognition rhythm to avoid missing or misrecognition due to changes in speech speed. This dynamic adaptation capability makes speech recognition no longer limited to standard pronunciation, but closer to real speech scenes.
For continuous noise in the environment, smart lecture tables usually use active noise reduction technology. For example, the humming of the air conditioner in the venue and the sound of traffic outside the window, these noises with fixed frequencies will be recorded in the "noise library" by the system in advance, and their impact will be weakened by reverse sound wave cancellation during recognition. Just like in the ocean of sound, the system can accurately locate and "shield" those persistent background sound waves, making the speaker's voice clearer and more prominent.
For sudden intermittent noises, such as the cough of the audience and the impact of objects falling, the system will start a real-time monitoring mechanism. When this type of noise occurs, the algorithm will quickly determine its duration and intensity. If the noise is short and the intensity does not exceed the threshold, it will automatically ignore and connect the previous and subsequent voice content; if the noise interference is large, it will pause the current recognition and wait for the sound to return to stability, avoiding misjudging the noise as valid voice and ensuring the coherence of the recognized content.
In multi-person interactive scenarios, the voice recognition function can distinguish different sound sources through voiceprint features. The podium will record the speaker's voiceprint information in advance. When the audience asks questions or interrupts, the system can filter out non-speaker voices based on the voiceprint differences to ensure that the main recognition object is always locked on the speaker. This "person-recognition" recognition logic ensures that the interactive communication during the speech will not interfere with the recognition of the core content, and maintains the system's focus on the speaker's voice.
In the face of echo interference, the smart lecture table solves the problem through acoustic echo cancellation technology. When the podium is connected to an audio device, the sound played may bounce back to the microphone to form an echo. The system will compare the difference between the output audio and the input audio in real time and accurately eliminate the echo signal. It is like installing a "filter" on the sound, retaining only the original sound emitted by the speaker in real time, avoiding the recognition confusion caused by the superposition of echo and original sound. This processing is particularly important in open places such as large lecture halls.
Emergency handling capabilities in extreme environments are also critical. When the environmental noise suddenly increases beyond the normal range, such as heavy rain accompanied by thunder outdoors, the system will automatically enhance the sensitivity of the microphone and start the extreme noise reduction mode at the same time, maintaining basic recognition functions by compressing the noise frequency band and amplifying effective voice. Although the recognition accuracy may decrease slightly at this time, it can ensure that the core semantics are not lost, giving the speaker time to adjust, reflecting the adaptability of technology in complex environments.
