Are there language-specific models for better accuracy?

Speech-to-text accuracy is crucial for the success of Voice AI applications, with real-world performance influenced by factors such as audio quality, speaker characteristics, and system configuration beyond the commonly cited Word Error Rate (WER). While WER measures the percentage of transcription errors, alternate metrics like Semantic WER, which focuses on meaning preservation, and Confidence Scoring, which assesses certainty of transcriptions, are also important. Real-world accuracy often falls short of ideal conditions due to variables like background noise, accents, and audio compression. To improve accuracy, optimizing audio input with quality microphones, managing recording environments, and using domain-specific models are recommended strategies. Language-specific models tend to yield better accuracy than multilingual ones due to their focus on a single language's nuances, but they can struggle with code-switching scenarios common in multilingual communities. AssemblyAI addresses these challenges with models like Universal-2 and Universal-3 Pro, providing a balance between broad language support and high accuracy in major languages.

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