Parametric watermarking is effected by modifying the linear predictor coefficients of speech. In this work, the parameter noise is analyzed when watermarked speech is subjected to additive white and colored noise in the time domain. The paper presents two detection techniques for parametric watermarking. The first approach uses the Neyman-Pearson criterion to solve a binary decision problem. In the second approach, discriminant functions based on the minimum-error-rate criterion are used to determine which one of the many watermkarks was embedded or if no watermark is present. Experiments with speech data are used to deter mine the false-alarm and missed detection rates.