## 1 Introduction  
Homophonic ambiguity is an inherent feature of tonal languages such as Chinese, and is a fundamental bottleneck for natural language processing (NLP) systems, especially in semantic understanding and cross-domain applications. Chinese, as a tonal language characterized by strict phoneme-glyph mappings, exhibits inherent homophonic ambiguity due to its monosyllabic structure. In Mandarin, monosyllabic homophones (e.g., /yj/ corresponds to the Chinese characters \(\mathbb{X}\), \(\mathbb{Z}\), \(\mathbb{i}\mathbb{X}\), etc.) form a unique phonetic-phonologicalmeaning decoupling system. By analogy with the English homophone system, the homophones in English as a deep orthography are divided into two categories: homophones (e.g., \"flower/flour\", derived from different roots in Latin and French); and homonyms (e.g., \"bank\", which has taken on different meanings through semantic drift). Statistical analysis shows that more than 62.2% [1] of Chinese characters have homophonic features, and speech disambiguation is a major influence in the semantic errors of speech recognition systems. Although intelligent voice interaction devices are emerging, traditional rule-based disambiguation methods rely on static dictionaries, perform poorly in dynamic environments [2], and are unable to solve the problem of cross-domain adaptation or low-frequency homophone recognition. This limitation severely hampers the development of dialog systems, speech-driven search engines, and assistive technologies for linguistically diverse populations, and necessitates new computational paradigms to address the interdependence between context and pitch. Critically, the manifestation of homophonic ambiguity is fundamentally exacerbated in speech-driven applications operating under dynamic environmental conditions\(-\)a dimension inadequately addressed by text-centric approaches. In tonal languages like Chinese, acoustic perturbations (e.g., ambient noise >65 dB, reverberation artifacts, or frequency-selective attenuation) induce phonetic confusions that systematically distort phonological representations. Empirical studies demonstrate that signal-to-noise ratio (SNR) degradation below 20 dB amplifies tonal misperception rates by 30-45% for Mandarin homophones (e.g., /m\(\overline{a}\)/ (\(\overline{b}\)/) vs. /ma/ (\(\frac{\Gamma M}{2}\))) due to the spectral masking of F0 contours and formant transitions. Such distortions propagate through automatic speech recognition (ASR) pipelines as phoneme substitution errors, where acoustically similar syllables (e.g., /shi/ corresponding to \(\frac{\pi}{\theta\Sigma}\), \(\overline{\Pi}\), \(\overline{\#}\)) become indistinguishable without contextual disambiguation. Crucially, conventional NLP models lack mechanisms to directly correlate these error patterns with real-time physical stimuli. Static error correction frameworks fail to adapt to transient acoustic events (e.g., impulsive machinery noise or wind interference), as their decision boundaries remain decoupled from the sensor-measured environmental dynamics that precipitate recognition failures. This sensorimotor gap necessitates cross-modal fusion architectures capable of ingesting non-linguistic sensory streams (e.g., spectrotemporal noise profiles, room impulse responses) to modulate disambiguation confidence thresholds. By aligning phonetic ambiguity resolution with instantaneous acoustic contexts, sensor-augmented systems can preemptively compensate for environmentally induced homophone confusions, transforming error correction from a reactive textual process into a proactive, physically grounded inference paradigm. While multimodal learning has emerged as a promising paradigm for ambiguity resolution, existing frameworks exhibit critical limitations in addressing environmentally induced homophone errors. Vision-language models (e.g., VL-BERT, LXMERT) leverage visual context to disambiguate textual polysemy, yet their efficacy collapses in acoustic-phonetic domains where sensory inputs lack explicit semantic mappings.