We propose new data selection approaches based on speaker discriminability features, including kurtosis and a set of nasality features which exploit spectral properties of nasal speech sounds. Data selected based on the speaker discriminability features are used to implement end-to-end speaker recognition systems, which produce significant improvements when combined with the baseline system (which uses the speech-only data regions determined by a speech/non-speech detector), where the optimal combination of systems produces roughly a 24% improvement over the baseline. Results suggest that focusing the modeling power on data regions selected via the kurtosis and nasality speaker discriminability features, part of which are often discarded in the speech/non-speech detection process, can improvement speaker recognition.
Bibliographic reference. Lei, Howard / Mirghafori, Nikki (2011): "Data selection with kurtosis and nasality features for speaker recognition", In INTERSPEECH-2011, 2753-2756.