The SP2 SCOPES Project on Speech Prosody

This is an overview of a Joint Research Project within the Scientific co-operation between Eastern Europe and Switzerland (SCOPES) Program of the Swiss National Science Foundation (SNFS) and Swiss Agency for Development and Cooperation (SDC). Within the SP2 SCOPES Project on Speech Prosody, in the course of the following two years, the four partners aim to collaborate on the subject of speech prosody and advance the extraction, processing, modeling and transfer of prosody for a large portfolio of European languages: French, German, Italian, English, Hungarian, Serbian, Croatian, Bosnian, Montenegrin, and Macedonian. Through the intertwined four research plans, synergies are foreseen to emerge that will build a foundation for submitting strong joint proposals for EU funding

Design of a Speech Corpus for Research on Cross-Lingual Prosody Transfer

Since the prosody of a spoken utterance carries information about its discourse function, salience, and speaker attitude, prosody mod- els and prosody generation modules have played a crucial part in text-to- speech (TTS) synthesis systems from the beginning, especially those set not only on sounding natural, but also on showing emotion or particular speaker intention. Prosody transfer within speech-to-speech translation is a recent research area with increasing importance, with one of its most important research topics being the detection and treatment of salient events, i.e. instances of prominence or focus which do not result from syn- tactic constraints, but are rather products of semantic or pragmatic level eects. This paper presents the design and the guidelines for the creation of a multilingual speech corpus containing prosodically rich sentences, ultimately aimed at training statistical prosody models for multilingual prosody transfer in the context of expressive speech synthesis

Switched adaptive quantiser for speech compression based on optimal companding and correlation

This study describes a novel adaptive quantiser based on the optimal companding technique. Adaptation is achieved by adjusting the input of the fixed or non-adaptive quantiser according to the estimated and quantised gain on each particular frame. In such a way better quantiser adaptation to the varying input statistics is provided. Selection of the appropriate bit rate is performed depending on the value of the correlation coefficient ρ on each frame. The decision thresholds for ρ are determined under the condition that the signal to quantisation noise ratio does not drop under 34.3ρdB, satisfying the G.712 standard quality of speech, while decreasing the bit rate. The information about the gain and about the chosen bit rate is then transferred as a side information to a decoder. Although this slightly increases the side information, the overall savings in the bit rate have shown to be substantial. Theoretical and experimental results are provided, which point out the benefits that can be achieved using the proposed algorithm

Speech and Computer16th International Conference, SPECOM 2014, Novi Sad, Serbia, October 5-9, 2014. Proceedings /

XVI, 484 p. 151 illus.online resource

DPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals

In this article DPCM (Differential Pulse Code Modulation) speech coding scheme with a simple switched first order predictor is presented. Adaptation of the quantizer to the signal variance is performed for each particular frame. Each frame is classified as high or low correlated, based on the value of the correlation coefficient, then the selection of the appropriate predictor coefficient and bitrate is performed. Low correlated frames are encoded with a higher bitrate, while high correlated frames are encoded with a lower bitrate without the objectionable loss in quality. Theoretical model and experimental results are provided for the proposed algorithm

MARVEL: Multimodal Extreme Scale Data Analytics for Smart Cities Environments

none47A Smart City based on data acquisition, handling and intelligent analysis requires efficient design and implementation of the respective AI technologies and the underlying infrastructure for seamlessly analyzing the large amounts of data in real-time. The EU project MARVEL will research solutions that can improve the integration of multiple data sources in a Smart City environment for harnessing the advantages rooted in multimodal perception of the surrounding environment.noneBajovic, Dragana; Bakhtiarnia, Arian; Bravos, George; Brutti, Alessio; Burkhardt, Felix; Cauchi, Daniel; Chazapis, Antony; Cianco, Claire; Dall'Asen, Nicola; Delic, Vlado; Dimou, Christos; Djokic, Djordje; Escobar-Molero, Antonio; Esterle, Lukas; Eyben, Florian; Farella, Elisabetta; Festi, Thomas; Geromitsos, Artemios; Giakoumakis, Giannis; Hatzivasilis, George; Ioannidis, Sotiris; Iosifidis, Alexandros; Kallipolitou, Theodora; Kalogiannis, Grigorios; Kiousi, Akrivi; Kopanaki, Despina; Marazakis, Manolis; Markopoulou, Stella; Muscat, Adrian; Paissan, Francesco; Lobo, Tomas Pariente; Pavlovic, Dusan; Raptis, Theofanis P.; Ricci, Elisa; Saez, Borja; Sahito, Farhan; Scerri, Kenneth; Schuller, Bjorn; Simic, Nikola; Spanoudakis, George; Tomasi, Alex; Triantafyllopoulos, Andreas; Valerio, Lorenzo; Villazan, Javier; Wang, Yiming; Xuereb, Andre; Zammit, JohanBajovic, Dragana; Bakhtiarnia, Arian; Bravos, George; Brutti, Alessio; Burkhardt, Felix; Cauchi, Daniel; Chazapis, Antony; Cianco, Claire; Dall'Asen, Nicola; Delic, Vlado; Dimou, Christos; Djokic, Djordje; Escobar-Molero, Antonio; Esterle, Lukas; Eyben, Florian; Farella, Elisabetta; Festi, Thomas; Geromitsos, Artemios; Giakoumakis, Giannis; Hatzivasilis, George; Ioannidis, Sotiris; Iosifidis, Alexandros; Kallipolitou, Theodora; Kalogiannis, Grigorios; Kiousi, Akrivi; Kopanaki, Despina; Marazakis, Manolis; Markopoulou, Stella; Muscat, Adrian; Paissan, Francesco; Lobo, Tomas Pariente; Pavlovic, Dusan; Raptis, Theofanis P.; Ricci, Elisa; Saez, Borja; Sahito, Farhan; Scerri, Kenneth; Schuller, Bjorn; Simic, Nikola; Spanoudakis, George; Tomasi, Alex; Triantafyllopoulos, Andreas; Valerio, Lorenzo; Villazan, Javier; Wang, Yiming; Xuereb, Andre; Zammit, Joha