Recent advances in generative speech have increased the need for automatic detection of obviously failed synthetic outputs. This is particularly important in clinical settings such as AVATAR therapy, in which schizophrenia patients engage with a computer-generated representation of their hallucinated voices and degraded synthesis may disrupt immersion and therapeutic engagement. We investigate whether low-dimensional, interpretable source-output acoustic features can provide a lightweight first-pass detector of degraded voice-cloning outputs. Motivated by source-filter models of speech, we first test median fundamental frequency (f0) as a source-related consistency measure, and compare it with vocal tract length (VTL) as a filter-related measure and Harmonics-to-Noise Ratio (HNR) as a noise-related descriptor. Human-labeled voice-cloning samples generated with two vocoder families, WaveRNN (n=54) and HiFi-GAN (n=40), were evaluated using an asymmetric thresholding procedure in the input-output feature space. For WaveRNN, f0 and HNR both achieved 85.2% accuracy, outperforming VTL (64.8%). For HiFi-GAN, HNR achieved 80.0% accuracy, followed by f0 at 77.5% and VTL at 67.5%. Sample-level overlap and spectrographic inspection showed that f0 and HNR capture partly distinct failure patterns, rather than providing redundant rankings of the same samples. These results show that simple source-output acoustic consistency measures can provide useful first-pass detection of degraded voice clones, and support the use of interpretable threshold-based screening in applications where failed synthetic speech must be rejected quickly.