Speech recognition systems handle repetition and redundancy in speech signals through a combination of acoustic modeling, language modeling, and post-processing techniques.
Acoustic Modeling: This component focuses on converting spoken sounds into phonemes or subword units. Repetition (e.g., "I I I think") is managed by analyzing the most probable sequence of words, often favoring the first occurrence or the most contextually appropriate one. Redundancy (e.g., "the the book") is resolved by selecting the most likely word sequence based on acoustic confidence scores.
Language Modeling: Language models predict the likelihood of word sequences, helping to filter out unnatural repetitions or redundancies. For example, if a user says, "The the book is is on the table," the language model will prioritize "The book is on the table" because it is statistically more probable.
Noise and Redundancy Filtering: Advanced systems use techniques like n-gram smoothing or neural network-based language models (e.g., Transformer-based models) to better understand context and discard irrelevant repetitions.
Post-Processing: After initial transcription, some systems apply text normalization or redundancy removal to refine the output. For instance, repeated words or filler phrases (e.g., "um," "uh") may be filtered out or condensed.
Example:
In cloud-based speech recognition, Tencent Cloud ASR (Automatic Speech Recognition) leverages deep learning models to handle such cases efficiently, providing accurate transcriptions even with imperfect speech input. It supports real-time and batch processing, making it suitable for applications like voice assistants, call centers, and transcription services.