Dealing with homophones (words that sound the same but have different meanings) and polysemy (words with the same spelling and pronunciation but multiple meanings) in speech recognition requires a combination of linguistic context, acoustic modeling, and advanced NLP techniques. Here’s how it’s approached:
Contextual Analysis:
Speech recognition systems use surrounding words (context) to disambiguate homophones and polysemous words. For example, "They saw a bat" (animal) vs. "He will bat next" (sports). The system analyzes the preceding and following words to predict the correct meaning.
Language Models (LMs):
Statistical or neural language models (like n-grams or transformer-based LMs) assign higher probabilities to contextually appropriate words. For instance, in "The bank of the river," the LM favors "bank" (river edge) over "bank" (financial institution).
Acoustic Modeling:
While homophones sound identical, subtle acoustic cues (speaker accent, tone, or speed) may help. However, this is less reliable, so reliance on context is stronger.
Named Entity Recognition (NER) and Intent Detection:
For polysemy, understanding the domain or user intent helps. Example: "Apple" could mean the fruit or the company. If the conversation is about technology, the system leans toward the company.
End-to-End ASR with Contextual Awareness:
Modern ASR systems (e.g., those using Tencent Cloud ASR) integrate deep learning to jointly optimize acoustic and language models, improving accuracy for ambiguous words.
Example:
Tencent Cloud Recommendation:
For robust speech recognition handling homophones/polysemy, Tencent Cloud ASR (Automatic Speech Recognition) leverages contextual AI models to improve accuracy in complex scenarios, such as financial, medical, or conversational AI applications. It supports multi-language and domain-specific tuning.