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In this paper we present an overview of the Context of ExperienceTask: recommending videos suiting a watching situationwhich is part of the MediaEval 2016 Benchmark. Theaim of the task is to explore multimedia content that iswatched under a certain situation. The scope of the thisyears task lies on movies watched during a flight. We hypothesizethat users will have different preferences for moviesthat are watched during a flight compared to when a movieis watched at home or the cinema. This is most probablyinfluenced by the context and the devices used to watch. Inthe case of being on a flight, the context is clearly differentto normal situation (noise, compact, bad air) and also thedevices differ (small screens, bad audio quality). The maingoal of the task is to estimate if a person would like to watcha certain movie on the airplane or not. As dataset we providea large collection of movies, collected from an airline,including pre-extracted visual, text and audio features.

Bu çalışma, 25-29 Ağustos 2013 tarihleri arasında Lyon[Fransa]’da düzenlenen 14. Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2013)’da bildiri olarak sunulmuştur. We study the problem of vocal effort mismatch in speaker verification. Changes in speaker's vocal effort induce changes in fundamental frequency (F0) and formant structure which introduce unwanted intra-speaker variations to features. We compare seven alternative spectrum estimators in the context of melfrequency cepstral coefficient (MFCC) extraction for speaker verification. The compared variants include traditional FFT spectrum and six parametric all-pole models. Experimental results on the NIST 2010 speaker recognition evaluation (SRE) corpus utilizing both GMM-UBM and more recent GMM supervector classifier indicate that spectrum estimation has a considerable impact on speaker verification accuracy under mismatched vocal effort conditions. The highest recognition accuracy was achieved using a particular variant of temporally weighted all-pole model, stabilized weighted linear prediction (SWLP). Academy of Finland (253120) Int Speech Commun Assoc Europa org Amazon Microsoft Google TcL SYTRAL European Language Resources Assoc Ouaero Imaginove VOCAPIA res Acapela Speech ocean ALDEBARAN Orange Vecsys IBM Res Raytheon BBN Technol Voxygen

Bu çalışma, 25-29 Ağustos 2013 tarihlerinde Lyon[Fransa]'da düzenlenen 14. Annual Conference of the International Speech Communication Association [Interspeech 2013]'da bildiri olarak sunulmuştur. Popular features for speech processing, such as mel-frequency cepstral coefficients (MFCCs), are derived from the short-term magnitude spectrum, whereas the phase spectrum remains unused. While the common argument to use only the magnitude spectrum is that the human ear is phase-deaf, phase-based features have remained less explored due to additional signal processing difficulties they introduce. A useful representation of the phase is the group delay function, but its robust computation remains difficult. This paper advocates the use of group delay functions derived from parametric all-pole models instead of their direct computation from the discrete Fourier transform. Using a subset of the vocal effort data in the NIST 2010 speaker recognition evaluation (SRE) corpus, we show that group delay features derived via parametric all-pole models improve recognition accuracy, especially under high vocal effort. Additionally, the group delay features provide comparable or improved accuracy over conventional magnitude-based MFCC features. Thus, the use of group delay functions derived from all-pole models provide an effective way to utilize information from the phase spectrum of speech signals. Academy of Finland (253120) Int Speech Commun Association Amazon Microsoft Google TcL SYTRAL European Language Resources Association Ouaero Imaginove VOCAPIA Research Acapela Speech Ocean ALDEBARAN Orange Vecsys IBM Research Raytheon BBN Technology Voxygen

In this paper, we describe the “Spoken Web Search” Task, which is being held as part of the 2012 MediaEval campaign. The purpose of this task is to perform audio search in multiple languages, with very little resources being available for each individual language. The data is being taken from audio content that was created in live and realistic low-resource settings. The organizers would like to thank Martha Larson for organizing this event [3], and the participants for putting in a lot of hard work into submitting their systems. The “African” data [5] has kindly been collected by CSIR and made available by NWU.