2015 |
Justin, Tadej; Štruc, Vitomir; Dobrišek, Simon; Vesnicer, Boštjan; Ipšić, Ivo; Mihelič, France Speaker de-identification using diphone recognition and speech synthesis Proceedings Article In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (IEEE FG): DeID 2015, pp. 1–7, IEEE 2015. Abstract | Links | BibTeX | Tags: DEID, FG, speech deidentification, speech recognition, speech synthesis, speech technologies @inproceedings{justin2015speaker,The paper addresses the problem of speaker (or voice) de-identification by presenting a novel approach for concealing the identity of speakers in their speech. The proposed technique first recognizes the input speech with a diphone recognition system and then transforms the obtained phonetic transcription into the speech of another speaker with a speech synthesis system. Due to the fact that a Diphone RecOgnition step and a sPeech SYnthesis step are used during the deidentification, we refer to the developed technique as DROPSY. With this approach the acoustical models of the recognition and synthesis modules are completely independent from each other, which ensures the highest level of input speaker deidentification. The proposed DROPSY-based de-identification approach is language dependent, text independent and capable of running in real-time due to the relatively simple computing methods used. When designing speaker de-identification technology two requirements are typically imposed on the deidentification techniques: i) it should not be possible to establish the identity of the speakers based on the de-identified speech, and ii) the processed speech should still sound natural and be intelligible. This paper, therefore, implements the proposed DROPSY-based approach with two different speech synthesis techniques (i.e, with the HMM-based and the diphone TDPSOLA- based technique). The obtained de-identified speech is evaluated for intelligibility and evaluated in speaker verification experiments with a state-of-the-art (i-vector/PLDA) speaker recognition system. The comparison of both speech synthesis modules integrated in the proposed method reveals that both can efficiently de-identify the input speakers while still producing intelligible speech. |
Justin, Tadej; Štruc, Vitomir; Žibert, Janez; Mihelič, France Development and Evaluation of the Emotional Slovenian Speech Database-EmoLUKS Proceedings Article In: Proceedings of the International Conference on Text, Speech, and Dialogue (TSD), pp. 351–359, Springer 2015. Abstract | Links | BibTeX | Tags: annotated data, dataset, dataset of emotional speech, EmoLUKS, emotional speech synthesis, speech synthesis, speech technologies, transcriptions @inproceedings{justin2015development,This paper describes a speech database built from 17 Slovenian radio dramas. The dramas were obtained from the national radio-and-television station (RTV Slovenia) and were given at the universities disposal with an academic license for processing and annotating the audio material. The utterances of one male and one female speaker were transcribed, segmented and then annotated with emotional states of the speakers. The annotation of the emotional states was conducted in two stages with our own web-based application for crowd sourcing. The final (emotional) speech database consists of 1385 recordings of one male (975 recordings) and one female (410 recordings) speaker and contains labeled emotional speech with a total duration of around 1 hour and 15 minutes. The paper presents the two-stage annotation process used to label the data and demonstrates the usefulness of the employed annotation methodology. Baseline emotion recognition experiments are also presented. The reported results are presented with the un-weighted as well as weighted average recalls and precisions for 2-class and 7-class recognition experiments. |