Performance Evaluation of Automatic Speaker Recognition Techniques for Forensic Applications

Beritelli, Francesco; Spadaccini, Andrea

doi:10.5772/52000

Cited by 1 publication

(1 citation statement)

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“…However, more types of models and classifiers should be tested in future works. Probably pauses would be better included into model using HMM chain, as applied in [25] or as a part of n-gram models [26].…”

Section: Discussionmentioning

confidence: 99%

Structure of pauses in speech in the context of speaker verification and classification of speech type

Igras-Cybulska

Ziółko

Żelasko

et al. 2016

J AUDIO SPEECH MUSIC PROC.

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Statistics of pauses appearing in Polish as a potential source of biometry information for automatic speaker recognition were described. The usage of three main types of acoustic pauses (silent, filled and breath pauses) and syntactic pauses (punctuation marks in speech transcripts) was investigated quantitatively in three types of spontaneous speech (presentations, simultaneous interpretation and radio interviews) and read speech (audio books). Selected parameters of pauses extracted for each speaker separately or for speaker groups were examined statistically to verify usefulness of information on pauses for speaker recognition and speaker profile estimation. Quantity and duration of filled pauses, audible breaths, and correlation between the temporal structure of speech and the syntax structure of the spoken language were the features which characterize speakers most. The experiment of using pauses in speaker biometry system (using Universal Background Model and i-vectors) resulted in 30 % equal error rate. Including pause-related features to the baseline Mel-frequency cepstral coefficient system has not significantly improved its performance. In the experiment with automatic recognition of three types of spontaneous speech, we achieved 78 % accuracy, using GMM classifier. Silent pause-related features allowed distinguishing between read and spontaneous speech by extreme gradient boosting with 75 % accuracy.

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Section: Discussionmentioning

confidence: 99%