Speaker-independent detection of child-directed speech

Identifying the distinct register that adults use when speaking to children is an important task for child development research. We present a fully automatic, speaker-independent system that detects child-directed speech. The two-stage system uses diarization-style voice activation techniques to extract speech segments followed by a supervised ν-SVM classifier trained on 1582 prosodic and log Mel energy features. The system significantly improves the state of the art, detecting child-directed speech with F1 of .66 (exact boundary) and .83 (within 1 second). A feature analysis confirms the importance of F0 features (especially 3rd quartile and range) as well as new features like the variance, kurtosis, and min of log Mel energy within a frequency band.