Evaluating Pre-trained Speech Encoders for Spontaneous Speech Detection and Out of Domain Synthetic Speech Generalisation in Indic Languages

Abstract

Transformer-based models have shown strong accuracy in distinguishing spontaneous from scripted speech and natural from synthetic speech, but these results are established on a narrow set of well-resourced language benchmarks and have not been extended across Indic languages, nor has embedding geometry been used to explain encoder behaviour or deepfake generalisation failure. We address these gaps by evaluating five frozen transformer encoders, AST, Vaani-FastConformer, Wav2vec2, Whisper and BEATs, across 22 Indic languages, and by conducting a multi-system TTS generalisation experiment across four TTS models. Beyond accuracy, we present language isolation probing and centroid proximity analysis. Probing reveals an encoder-dependent trade-off between language-discriminability and spontaneity detection. Centroid analysis shows that out-of-domain generalisation is predicted by a training system's proximity to unseen TTS embeddings, not its distance from natural speech, a finding with direct implications for training data selection in real-world deepfake detectors.