Towards Confidence Measures on Fundamental Frequency Estimations

Boyuan Deng (1) , Denis Jouvet (1) , Yves Laprie (1) , Ingmar Steiner (2) , Aghilas Sini (1)

1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
2 DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence

The fundamental frequency is one of the prosodic parameters , and many algorithms have been developed for estimating the fundamental frequency of speech signals. Most of them provide good results on good quality speech signals, but their performance degrades when dealing with noisy signals. Moreover, although some provide a probability for the voicing decision, none of them indicate how reliable the estimated fundamental frequency is. In this paper, we investigate the computation of a confidence (or reliability) measure on the estimated fundamental frequency values. A neural network based approach is proposed for computing the posterior probability that the estimated fundamental frequency is correct. Experiments are conducted on the PTDB-TUG pitch-tracking database, using three fundamental frequency estimation algorithms .

Mots clés: Pitch; fundamental frequency; estimation; confidence measures

Domaines: Traitement du signal et de l'image [eess.SP]

Boyuan Deng, Denis Jouvet, Yves Laprie, Ingmar Steiner, Aghilas Sini. Towards Confidence Measures on Fundamental Frequency Estimations. IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2017, New Orleans, United States. ⟨hal-01493168⟩