## 5 Sensor-Based Speech Recognition
### Sensor-Text Interaction  
In this study, intelligent disambiguation decision-making for environment sensing is realized through the closed-loop coupling design of acoustic sensors and semantic processing. The sensor module continuously collects three-dimensional environmental parameters of noise, temperature, and humidity in a 1 s cycle, and constructs a temporal feature matrix by storing 60 s historical data in a ring buffer. In the speech recognition stage, the system uses perceptually weighted spectral subtraction to dynamically reduce the input audio, and its filter parameters are adaptively adjusted according to the real-time noise intensity: when the noise exceeds 65 dB, it activates the deep noise reduction mode, predicts the noise spectral characteristics through the BiLSTM network, and constructs the Time-Varying Spectral Mask, which improves the speech SNR to 18.6 dB, and then increases the SNR to 18.6 dB, which is a very high SNR. The Time-Varying Spectral Mask is constructed to improve the speech signal-to-noise ratio to 18.6 dB, reducing the misrecognition rate of homophones from the source. The deep integration of acoustic sensors and semantic disambiguation is reflected in three dimensions: at the feature level, the system builds a joint embedding space of environmental parameters and speech features, and establishes a nonlinear mapping between noise intensity and homophone error rate through the cross-modal attention mechanism, and the experiments show that when the noise is more than 65 dB, the error correction confidence threshold is automatically relaxed to 0.07; at the decision-making level, the error correction threshold is automatically relaxed to 0.07; at the decision-making level, the noise level is reduced to 18.6 dB by the Time Varying Spectral Mask. 0.07; at the decision-making level, we design an Environment-aware Dynamic Lexicon (EDL), which adjusts the weights of candidate words according to real-time temperature and humidity data, for example, to improve the discriminative boundaries of error-prone pairs of words such as \"\(\frac{\text{Var}_{\text{DDL}}}{\text{Var}_{\text{DDL}}}\)\" in a high-temperature (>35 \({}^{\circ}\)C) environment; at the interaction level, we develop an anomaly detection module based on sensor data streams, and when a sudden and unexpected event is detected, the error detection threshold is automatically relaxed to 0.07; at the interaction level, we develop anomaly detection modules based on the sensor data stream, which automatically triggers the speech re-recognition mechanism when sudden noise (e.g., equipment whistling) is detected, and generates more robust recognition results through the Multiple Hypothesis Fusion (MHF) algorithm.  
The above experiments show that this technical solution can already be applied to speech interaction systems in the fields of intelligent manufacturing and telemedicine, which significantly improves the semantic accuracy of Chinese speech recognition. Future work will explore the automatic optimization of models based on Neural Architecture Search (NAS) and the construction of a multi-sensor federated learning framework.  
The proposed Environmental-Driven Lexicon (EDL) mechanism demonstrates significant thermodynamic regularization effects on disambiguation entropy. As ambient temperature exceeds the activation threshold, EDL dynamically amplifies the contextual weighting coefficient (\(\beta\)) by 30% for thermal-relevant lexemes (e.g., \"\(\  
show our confusion matrix and normalized confusion matrix. Figure 8 shows the flowchart of our program.  
Figure 6: Confusion matrix.  
Figure 7: Normalized confusion matrix.