This paper proposes a tri-modal speech reconstruction method that synergistically fuses laser speckle vibrometry, depth-camera articulatory capture, and ultra-wideband (UWB) radar. By exploiting the RVTALL multimodal dataset, the method extracts highly complementary physical information: laser signals capture physiological vibrations homologous to voice sources, depth cameras record articulatory geometry, and UWB radar detects radio-frequency micro-motions. To maximize the utility of these heterogeneous data streams, the framework adopts a two-stage training paradigm, where modality-specific encoders are pretrained with an auxiliary classification objective and then frozen for label-free log-mel spectrogram regression. A hybrid loss combining global spectral consistency and local dynamic constraints is designed to optimize fine-grained formant transitions and energy onsets. The proposed method demonstrates significant superiority over single-modality approaches without requiring any textual or phonetic supervision. Experimental results under a strict leave-two-subjects-out protocol show that the full tri-modal system achieves an 81.7% word-level Whisper accuracy, a 16.3% absolute improvement over the single laser modality (65.4%). Furthermore, the wav2vec 2.0 sentence-level cosine similarity reaches 0.932 (compared to 0.799 for laser only), and the word and sentence mean opinion scores (MOS) reach 3.08 and 2.67, respectively. These results confirm that the rich physical complementarity within the tri-modal dataset enables highly intelligible, natural, and noise-resilient speech recovery.