How to evaluate the accuracy of a speech recognition system?

To evaluate the accuracy of a speech recognition system, the most common metric is Word Error Rate (WER). WER measures the number of errors (substitutions, insertions, and deletions) between the recognized text and the reference (ground truth) text, normalized by the total number of words in the reference. The formula for WER is:

WER = (S + I + D) / N
Where:

• S = Number of substitutions (wrong words)
• I = Number of insertions (extra words)
• D = Number of deletions (missing words)
• N = Total number of words in the reference text

A lower WER indicates higher accuracy. For example, if the reference text is "The quick brown fox" and the system outputs "The quick brown dog," there is 1 substitution (fox → dog), resulting in a WER of 1/4 = 0.25 (25%).

Other metrics include:

• Character Error Rate (CER): Similar to WER but calculates errors at the character level, useful for languages with no spaces (e.g., Chinese).
• Accuracy: The percentage of correctly recognized words (1 - WER).

For speech recognition systems in cloud environments, Tencent Cloud offers Automatic Speech Recognition (ASR) services with built-in evaluation tools to measure accuracy. These services support real-time and batch processing, with optimizations for different industries (e.g., call centers, media transcription). You can compare the ASR output with ground truth data to compute WER or CER using Tencent Cloud’s AI tools or external scripts.