Speech Recognition: Models, Challenges, Solutions

Speech recognition technology has advanced by simplifying the traditional automatic speech recognition (ASR) pipeline into a single neural network model that maps audio directly to text, eliminating the need for separate acoustic, pronunciation, and language models. The choice of model architecture—whether CTC, attention encoder-decoder, or RNN-T—affects performance trade-offs in terms of latency, streaming capabilities, and accuracy challenges, particularly in handling rare terms and out-of-vocabulary (OOV) issues. In production environments, speech recognition models often face challenges such as audio format mismatches, domain-specific vocabulary gaps, and performance degradation in noisy conditions. Runtime vocabulary adaptation, such as keyterm prompting, provides a quick fix for domain-specific vocabulary issues without the need for retraining, whereas custom model training is necessary for addressing acoustic discrepancies. The decision between streaming and batch processing should be guided by the latency budget rather than use-case labels, with streaming suited for real-time applications and batch processing offering greater accuracy for post-event analysis. To ensure reliability, it is crucial to validate the chosen architecture against real-world audio samples, focusing on metrics that align with specific business outcomes.