The biometric and forensic performance of automatic speaker recognition systems degrades under noisy and short probe utterance conditions. Score normalization is an effective tool taking into account the mismatch of reference and probe utterances. In an adaptive symmetric score normalization scheme for state-ofthe-art i-vector recognition systems, a set of cohort speakers are employed to calcul...

In this paper, we investigate model selection and channel variability issues on a text-dependent single utterance (TDSU) speaker verification application. Due to the lack of an appropriate database for the task, a multichannel speaker recognition database, which consists of multiple recordings of a single Turkish utterance, is collected. The first set of experiments is devoted to model selectio...

This paper demonstrates the framework and results from the team “Florida International University University of Miami (FIU-UM)” in the TRECVID 2017 [1] Ad-hoc Video Search (AVS) task [2]. The following four runs were submitted: • M D FIU UM.17 1: CNN features + Linear SVM • M D FIU UM.17 2: CNN features + Linear SVM + Scores from other groups • M D FIU UM.17 3: CNN features + Linear SVM + Recti...

This paper presents an overview of a state-of-the-art text-independent speaker verification system using score normalization. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Normalization of scores is then explai...

In this paper we report our work for normalization of noisy text in Twitter data. The method we propose is hybrid in nature that combines machine learning with rules. In the first step, supervised approach based on conditional random field is developed, and in the second step a set of heuristics rules is applied to the candidate wordforms for the normalization. The classifier is trained with a ...

The development of spoken term detection (STD) techniques, which detect a given word or phrase from spoken documents, is widely conducted in order to realize easy access to large amount of multimedia contents including speech This paper describes improvement of the STD method which is based on the vector quantization (VQ) and has been proposed in NTCIR-9 SpokenDoc. Spoken documents are represen...

This paper re-examines the speech rhythm of Beijing Chinese and Pisa Italian by means of the Control/Compensation Index (CCI), with a view to normalizing the speech data, in order to reduce the effect of the rate factor. Two metrics were applied: (a) DnCCI, an adaptation to the CCI model of the nPVI normalization strategy; (b) SnCCI, a z-score normalization, which takes into account the actual ...

In this paper we discuss a speaker identification approach, called ASR-dependent speaker identification, that incorporates phonetic knowledge into the models for each speaker. This approach differs from traditional methods for performing textindependent speaker identification, such as global Gaussian mixture modeling, that typically ignore the phonetic content of the speech signal. We introduce...

Score normalization is indispensable in distributed retrieval and fusion or meta-search where merging of result-lists is required. Distributional approaches to score normalization with reference to relevance, such as binary mixture models like the normal-exponential, suffer from lack of universality and troublesome parameter estimation especially under sparse relevance. We develop a new approac...

In this paper we discuss a speaker identification approach, called ASR-dependent speaker identification, that incorporates phonetic knowledge into the models for each speaker. This approach differs from traditional methods for performing textindependent speaker identification, such as global Gaussian mixture modeling, that typically ignore the phonetic content of the speech signal. We introduce...