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Recent developments in inference and learning in Dynamic Bayesian networks (DBN) allow their use in real-world applications is the first successful application of DBNs to a large scale speech recognition problem. Even if their progress is huge, those models lack a discriminatory ability especially on speech recognition such as the Hidden Markov models (HMM). In this paper, we present the performance...
Five expressions are commonly considered to characterize human emotional states: Happiness, Surprise, Anger, Sadness and Neutral. Different measures can be extracted from speech signals to characterize these expressions, for example the pitch, the energy, the SPI and the speech rate. Automatic classification of the five expressions based on these features shows a great confusion between Anger, Surprise...
Speaker recognition has many applications such as access control, person authentication systems, forensics etc. In forensic applications, questioned recording may be received through different channels, noisy conditions and with cases of voice forgery, which make speaker recognition a challenging task. State of the art speaker recognition systems use spectral features which are susceptible to channel...
This paper investigates how to integrate multi-modal features for story boundary detection in broadcast news. The detection problem is formulated as a classification task, i.e., classifying each candidate into boundary/non-boundary based on a set of features. We use a diverse collection of features from text, audio and video modalities: lexical features capturing the semantic shifts of news topics...
Token-based approaches have proven quite effective for spoken language identification (LID). Traditionally, Speech utterances are first decoded into token sequences, and then LID tasks are performed on these token sequences by either n-gram language models or support vector machines. In this paper, we propose a hierarchical system design, which utilizes a group of bayesian logistic regression models...
Phoneme recognition is an essential component of any robust speech decoder and has been tackled by many researchers. Speech feature extraction constitutes the front end module of any speech decoder: it plays an essential role and has a strong impact on the recognition performance. The research community is aggressively searching for more powerful solutions which combine the existing feature extraction...
We introduce a novel strategy for speaker verification based on the conception of a classifier which is independent of the target speaker, as opposed to traditional systems where the classifier is always target dependent. The basic principle is to build a system that decides whether two sequences were pronounced by the same speaker. In our view, this system is aimed to complement traditional ones...
Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification...
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