In the last years, the task of Query-by-Example Spoken Term Detection (QbE-STD), which aims to find occurrences of a spoken query in a set of audio documents, has gained the interest of the research community for its versatility in settings where untranscribed, multilingual and acoustically unconstrained spoken resources, or spoken resources in low-resource languages, must be searched. This paper describes and reports experimental results for a QbE-STD system that achieved the best performance in the recent Spoken Web Search (SWS) evaluation, held as part of MediaEval 2013. Though not optimized for speed, the system operates faster than real-time. The system exploits high-performance phone decoders to extract frame-level phone posteriors (a common representation in QbE-STD tasks). Then, given a query and a audio document, a distance matrix is computed between their phone posterior representations, followed by a newly introduced distance normalization technique and an iterative Dynamic Time Warping (DTW) matching procedure with some heuristic prunings. Results show that remarkable performance improvements can be achieved by using multiple examples per query and, specially, through the late (score-level) fusion of different subsystems, each based on a different set of phone posteriors.

This paper presents an alternative feature set to the traditional MFCC-SDC used in acoustic approaches to Spoken Language Recognition: the log-likelihood ratios of phone posterior probabilities, hereafter Phone Log-Likelihood Ratios (PLLR), produced by a phone recognizer. In this work, an iVector system trained on this set of features (plus dynamic coefficients) is evaluated and compared to (1) an acoustic iVector system (trained on the MFCC-SDC feature set) and (2) a phonotactic (Phone-lattice-SVM) system, using two different benchmarks: the NIST 2007 and 2009 LRE datasets. iVector systems trained on PLLR features proved to be competitive, reaching or even outperforming the MFCC-SDC-based iVector and the phonotactic systems. The fusion of the proposed approach with the acoustic and phonotactic systems provided even more significant improvements, outperforming state-of-the-art systems on both benchmarks.

This paper evaluates the performance of the twelve primary systems submitted to the evaluation on speaker verification in the context of a mobile environment using the MOBIO database. The mobile environment provides a challenging and realistic test-bed for current state-of-the-art speaker verification techniques. Results in terms of equal error rate (EER), half total error rate (HTER) and detection error trade-off (DET) confirm that the best performing systems are based on total variability modeling, and are the fusion of several sub-systems. Nevertheless, the good old UBM-GMM based systems are still competitive. The results also show that the use of additional data for training as well as gender-dependent features can be helpful.

The Mel-frequency cepstral coefficients (MFCC) and their derivatives are commonly used as acoustic features for speaker recognition. The issue arises of whether some of those features are redundant or dependent on other features. Probably, not all of them are equally relevant for speaker recognition. Reduced feature sets allow more robust estimates of the model parameters. Also, less computational resources are required, which is crucial for real-time speaker recognition applications using low-resource devices. In this paper, we use feature weighting as an intermediate step towards feature selection. Genetic algorithms are used to find the optimal set of weights for a 38-dimensional feature set, consisting of 12 MFCC, their first and second derivatives, energy and its first derivative. To evaluate each set of weights, speaker recognition errors are counted over a validation dataset. Speaker models are based on empirical distributions of acoustic labels, obtained through vector quantization. On average, weighting acoustic features yields between 15% and 25% error reduction in speaker recognition tests. Finally, features are sorted according to their weights, and the K features with greatest average ranks are retained and evaluated. We conclude that combining feature weighting and feature selection allows to reduce costs without degrading performance

The synchronization of text transcripts with audio tracks is typically solved by forced alignment at the phonetic level. However, when dealing with either very long audio tracks or acoustically inaccurate text transcripts, more complex methods are needed, usually based on heavy and costly ASR systems. In a previous work, we showed that a simple and lightweight method could be effectively applied, based on a free phonetic decoding of the speech signal and the alignment of the free and reference phonetic sequences, allowing the transfer of timestamps from the former to the latter. This method has yielded competitive results on the Hub4-97 dataset and is currently applied to synchronize the videos and minutes of the Basque Parliament plenary sessions. In this paper, probabilistic kernels (similarity functions) are applied, based on the hypothesis that a confusion matrix computed from a large corpus of speech conveys key information about the behavior of the phonetic decoder, and that the probabilistic interpretation of this information may help design informative kernels leading to improved alignments. The probabilistic kernels proposed in this work outperform our baseline kernels and other alternatives, including a reference ASR-based approach and a knowledge-based kernel, in experiments on the Hub4-97 dataset.