Data Labeling - Pareto.AI Blog

Complexity: The traditional hybrid approach involves multiple components such as acoustic models, language models, and lexicons, making the system complex and difficult to optimize.
Data Dependency: Building accurate models in the traditional hybrid approach often requires large amounts of labeled training data, which may be expensive and time-consuming to acquire.
Limited Context: Traditional hybrid models may struggle to capture long-range dependencies and contextual information in speech, leading to errors in transcription, especially in conversational or ambiguous contexts.
Maintenance and Tuning: Fine-tuning and maintaining traditional hybrid ASR systems require significant expertise and effort, including regular updates to adapt to language use and acoustic environment changes.
Robustness to Variability: Traditional hybrid models may struggle to adapt to variations in speech characteristics such as accent, speaking rate, or background noise, leading to decreased accuracy in real-world environments.

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