By Juan Luis Navarro Mesa, Alfonso Ortega, António Teixeira, Eduardo Hernández Pérez, Pedro Quintana Morales, Antonio Ravelo García, Iván Guerra Moreno, Doroteo T. Toledano (eds.)
This publication constitutes the refereed court cases of the IberSPEECH 2014 convention, held in Las Palmas de Gran Canaria, Spain, in November 19-21, 2014. The 29 papers awarded have been conscientiously reviewed and chosen from 60 submissions. The papers are equipped in topical sections on speech construction, research, coding and synthesis; speaker and language characterization; automated speech acceptance; speech of language applied sciences in numerous program fields.
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Extra info for Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings
Modeling and interpolation of Austrian German and Viennese dialect in HMM-based speech synthesis. Speech Communication 52(2), 164–179 (2010) 3. : HMM-based Tianjin Dialect speech synthesis using bilateral question Set. In: 2011 IEEE International Workshop on Machine Learning for Signal Processing, Beijing, pp. 1–4 (2011) 4. : Synthesis of dialect speech for an underresourced language. In: SATNAC, Western Cape (2012) 5. : Simultaneous modeling of spectrum, pitch and duration in HMMbased speech synthesis.
1. Image frames, for speaker CM, acquired along the production of [sal5], depicting the extracted vocal tract contour in a diﬀerence diagram. At this moment, the framework considers the diﬀerence for seven features: tongue back (TB), velum (VEL), tongue dorsum (TD), tongue tip (TT), lip aperture (LA), lip protrusion (LP) and pharyngeal wall (Ph). The diﬀerences, for each of the features, are computed using various methods. For example, for the tongue back, tongue dorsum, velum and pharingeal wall, the Pratt index is used  to compare contour segments.
Leaving buzziness aside, these listeners did not find a criterion for discrimination in the female case. However, for the male voice, there were some subtle discontinuities in a few phonetic transitions which we believe related to a lower amount of training data and a less accurate phonetic labeling. According to the perception of the second group of listeners, long analysis windows alleviate the negative impact of the mentioned discontinuities (at the expense of some extra buzziness to which they were not very sensitive).