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We present a study on purely data-based recognition of animal sounds, performing evaluation on a real-world database obtained from the Humboldt-University Animal Sound Archive. As we avoid a preselection of friendly cases, the challenge for the classifiers is to discriminate between species regardless of the age or stance of the animal. We define classification tasks that can be useful for information...
Features generated by Non-Negative Matrix Factorization (NMF) have successfully been introduced into robust speech processing, including noise-robust speech recognition and detection of non-linguistic vocalizations. In this study, we introduce a novel tandem approach by integrating likelihood features derived from NMF into Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs)...
We present a novel and unique combination of algorithms to detect the gender of the leading vocalist in recorded popular music. Building on our previous successful approach that enhanced the harmonic parts by means of Non-Negative Matrix Factorization (NMF) for increased accuracy, we integrate on the one hand a new source separation algorithm specifically tailored to extracting the leading voice from...
We propose a novel multi-stream framework for continuous conversational speech recognition which employs bidirectional Long Short-Term Memory (BLSTM) networks for phoneme prediction. The BLSTM architecture allows recurrent neural nets to model long-range context, which led to improved ASR performance when combined with conventional triphone modeling in a Tandem system. In this paper, we extend the...
This demonstration aims to showcase the recently completed SEMAINE system. The SEMAINE system is a publicly available, fully autonomous Sensitive Artificial Listeners (SAL) system that consists of virtual dialog partners based on audiovisual analysis and synthesis (see http://semaine.opendfki.de/wiki). The system runs in real-time, and combines incremental analysis of user behavior, dialog management,...
Most research efforts dealing with recognition of emotion-related states from the human speech signal concentrate on acoustic analysis. However, the last decade's research results show that the task cannot be solved to complete satisfaction, especially when it comes to real life speech data and in particular to the assessment of speakers' valence. This paper therefore investigates novel approaches...
The fusion of multiple recognition engines is known to be able to outperform individual ones, given sufficient independence of methods, models, and knowledge sources. We therefore investigate late fusion of different speech-based recognizers of emotion. Two generally different streams of information are considered: acoustics and linguistics fed by state-of-the-art automatic speech recognition. A total...
This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional context information to reliably identify phonemes, whereas the DBN is able to discriminate...
Data sparseness is an ever dominating problem in automatic emotion recognition. Using artificially generated speech for training or adapting models could potentially ease this: though less natural than human speech, one could synthesize the exact spoken content in different emotional nuances - of many speakers and even in different languages. To investigate chances, the phonemisation components Txt2Pho...
Recently great interest has been shown in the visual surveillance of public transportation systems. The challenge is the automated analysis of passenger's behaviors with a set of visual low-level features, which can be extracted robustly. On a set of global motion features computed in different parts of the image, here the complete image, the face and skin color regions, a classification with Support...
As automatic emotion recognition based on speech matures, new challenges can be faced. We therefore address the major aspects in view of potential applications in the field, to benchmark today's emotion recognition systems and bridge the gap between commercial interest and current performances: acted vs. spontaneous speech, realistic emotions, noise and microphone conditions, and speaker independence...
Video based analysis of a persons' mood or behavior is in general performed by interpreting various features observed on the body. Facial actions, such as speaking, yawning or laughing are considered as key features. Dynamic changes within the face can be modeled with the well known hidden Markov models (HMM). Unfortunately even within one class examples can show a high variance because of unknown...
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