By Andrey Ronzhin, Rodmonga Potapova, Nikos Fakotakis
This publication constitutes the refereed lawsuits of the seventeenth overseas convention on Speech and computing device, SPECOM 2015, held in Athens, Greece, in September 2015. The fifty nine revised complete papers awarded including 2 invited talks have been rigorously reviewed and chosen from 104 preliminary submissions. The papers conceal a variety of subject matters within the sector of machine speech processing corresponding to attractiveness, synthesis, and figuring out and comparable domain names together with sign processing, language and textual content processing, multi-modal speech processing or human-computer interaction.
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Extra resources for Speech and Computer: 17th International Conference, SPECOM 2015, Athens, Greece, September 20-24, 2015, Proceedings
Each hypothesis is aligned to its reference, resulting in a list of pairs confused by the ASR system together with the probability of confusion. The artiﬁcial examples are generated from a text source which do not need to be associated with any acoustic component, thus is easier to obtain than manually transcribed recordings. Application of the CM yields a lattice of possible hypotheses that could be output by the ASR system if that sentence were to be uttered. The most probable N paths can be used as training data for the DLM training, along with their associated source text as the reference.
References 1. : Feature combination approaches for discriminative language models. In: Proceedings of Interspeech, Florence, Italy (2011) 2. : Turkish broadcast news transcription and retrieval. IEEE Trans. Audio Speech Lang. Process. 17(5), 874–883 (2009) 3. : Discriminative language modeling with linguistic and statistically derived features. IEEE Trans. Audio Speech Lang. Process. 20(2), 540–550 (2012) 4. : Improved natural language learning via variance-regularization support vector machines.
4. Noise reduction level for artiﬁcial incoherent and real combined (coherent & diﬀuse) noise. 1 MA Directivity Patterns MA directivity patterns for a harmonical signal with the frequency f = 2000 Hz for the FBF method and three adaptive methods (1), (6) and (7–8) are shown in Fig. 2. All adaptive methods have better performance than FBF: the main lobes are narrower and the sidelobes are smaller. First we should point out that the MCA method [2,10] gives the narrowest main lobe. Second, for both Zelinski post-ﬁlters (1) and (6) sidelobes are completely absent.