By Jesús Villalba, Eduardo Lleida, Alfonso Ortega, Antonio Miguel (auth.), Doroteo Torre Toledano, Alfonso Ortega Giménez, António Teixeira, Joaquín González Rodríguez, Luis Hernández Gómez, Rubén San Segundo Hernández, Daniel Ramos Castro (eds.)
This quantity constitutes the refereed complaints of the Spanish convention, IberSPEECH 2012: Joint VII “Jornadas en Tecnología del Habla” and III Iberian SLTech Workshop, held in Madrid, Spain, in November 21-23, 2012. The 29 revised papers have been conscientiously reviewed and chosen from eighty submissions. The papers are geared up in topical sections on speaker characterization and popularity; audio and speech segmentation; pathology detection and speech characterization; discussion and multimodal platforms; robustness in computerized speech attractiveness; purposes of speech and language technologies.
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Additional info for Advances in Speech and Language Technologies for Iberian Languages: IberSPEECH 2012 Conference, Madrid, Spain, November 21-23, 2012. Proceedings
References 1. : Application-independent evaluation of speaker detection. Comp. Speech Lang. (20), 230–275 (2006) 2. : Front-End Factor Analysis for Speaker Verification. IEEE Trans. on Audio, Speech and Lang. Proc. 19(4), 788–798 (2011) 3. : Forensic speaker recognition using traditional features comparing automatic and human-in-the-loop formant tracking. In: Proceedings of Interspeech 2009, pp. 2343–2346 (September 2009) 4. : Statistical modeling of heterogeneous features for speech processing tasks.
Another issue is what should be the selected units to be fused. Two strategies have been used in this work. The first of them is to select the n-best performing units by setting a threshold for the EER of the units to be fused, leaving out those performing worse. However, this procedure do not guaranty that the best fused system will be achieved because some units with lower performance by itself could contribute to the fused system if its LR’s are sufficiently low correlated with those produced by the other units to be fused.
E. Melcepstral coefficients. Despite this, its performance is significantly enhanced in terms of subjective quality, because the degree of oversmoothing is effectively reduced and the converted voice sounds more natural. The remainder of the paper is structured as follows. Section 2 contains a brief description of the fundamentals of GMM-based voice conversion, including a mathematical interpretation of the oversmoothing effect. In section 3 we show the details of one of the most popular FW training methods.
Advances in Speech and Language Technologies for Iberian Languages: IberSPEECH 2012 Conference, Madrid, Spain, November 21-23, 2012. Proceedings by Jesús Villalba, Eduardo Lleida, Alfonso Ortega, Antonio Miguel (auth.), Doroteo Torre Toledano, Alfonso Ortega Giménez, António Teixeira, Joaquín González Rodríguez, Luis Hernández Gómez, Rubén San Segundo Hernández, Daniel Ramos Castro (eds.)