TL;DR: The performances of several classification methods are compared, including Gaussian Mixture Model–Universal Background Model (GMM–UBM), GMM–Support Vector Machine (G MM–SVM) and i-vector based approaches, and the utility of different frequency bands for speaker, age-group and gender recognition from children’s speech is assessed.

TL;DR: This paper proposed a new approach to detect synthetic speech using score-level fusion of front-end features namely, constant Q cepstral coefficients (CQCCs), all-pole group delay function (APGDF) and fundamental frequency variation (FFV), which outperforms all existing baseline features for both known and unknown attacks.

TL;DR: A detailed analysis of neural versus phrase-based statistical machine translation outputs, leveraging high quality post-edits performed by professional translators on the IWSLT data provides useful insights on what linguistic phenomena are best modelled by neural models.

TL;DR: It is found that DNN-based ASR reaches human performance for single-channel, small-vocabulary tasks in the presence of speech-shaped noise and in multi-talker babble noise, which is an important difference to previous human-machine comparisons.

TL;DR: A paraphrase identification system that represents each pair of sentence as a combination of different similarity measures that extract lexical, syntactic and semantic components of the sentences encompassed in a graph is proposed.

TL;DR: Experiments on the core test condition 5 of NIST SRE 2010 show that comparable results with conventional i-vectors are achieved with a clearly lower computational load in the vector extraction process.

TL;DR: The results presented here suggest that substantial reduction in WER is achieved with clean training, and the uncertainty weighting method reduced the gap between clean and multi-noise/multi-condition training.

TL;DR: This paper proposed a new deep neural network that explores recurrent models to capture word sequences within sentences, and further study the impact of pretrained word embeddings on the performance of the proposed approach.

TL;DR: In this paper, the rank-1 constrained multichannel Wiener filter is employed for noise reduction and a new constant residual noise power constraint is derived which enhances the recognition performance.

TL;DR: In this article, a domain-invariant linear discriminant analysis (DI-LDA) technique was proposed to compensate domain mismatch from both LDA and PLDA subspaces.

TL;DR: Two approaches to tackling dialogue management as a reinforcement learning task are presented, whereby a recurrent neural network is utilised as a task success predictor which is pre-trained from off-line data to estimate task success during subsequent on-line dialogue policy learning.

TL;DR: An empirical study on POS tagging for Vietnamese social media text is presented, which shows several challenges compared with tagging for general text and the semi-supervised model outperformed, in terms of accuracy, the version of vnTagger trained on the same Facebook dataset, showing the usefulness of word cluster features.

TL;DR: This work presents a novel prototype Rule Based Machine Translation (RBMT) system for the creation of large and quality written Greek Sign Language (GSL) glossed corpora from Greek text and stresses that Language Models for written GSL gloss are missing from the scientific literature, thus this work is pioneer in this field.

TL;DR: A corpus similarity measure based on PCA-ranked features answers the question which corpora should be included into joint training and outperforms all other combinations of corpora.

TL;DR: A novel prosody teaching system where intensity (accent), intonation and rhythm are presented visually for the students as visual feedback and automatic assessment scores are given jointly and separately for the goodness of intonations and rhythm is introduced.

TL;DR: This paper addresses the viability of using Automatic Speech Recognition errors as the predictor of difficulties in speech segments, thereby exploiting them to improve Partial and Synchronized Caption (PSC), and proposes the use of ASR systems as a model of L2 listeners and hypothesize that ASR errors can predict challenging speech segments for these learners.

TL;DR: Both raw speech samples and mel frequency cepstral coefficients are used as an initial representation for feature extraction and a transformation function known as weighted decomposition (WD) of principal components is used to emphasize the discriminative information present in the PCA-based dictionary.

TL;DR: The use of Web texts for language modeling is shown to significantly improve both speech recognition and keyword spotting performance, and combining full-word and subword units leads to the best keyword spotting results.

TL;DR: Speech modification strategies based on reallocating energy statically across the spectrum using masker-specific spectral weightings are investigated, indicating that energy-neutral spectral weighting is a highly-effective near-end speech enhancement approach that places minimal demands on detailed masker estimation.

TL;DR: This work proposes an unsupervised method—RankUp—that enhances graph-based keyphrase extraction approaches by applying an error-feedback mechanism similar to the concept of backpropagation, and shows that error- feedback propagation can boost the quality of keyphrases in graph- based keyphrase extractions techniques.

TL;DR: This paper provides further analysis on the proposed nonparametrically trained PLDA as well as introduces a duration variability modeling technique in the estimation of the within-speaker scatter matrix as to compensate for the effect of limited speech data.

TL;DR: This study adopted Chinese radical information for sentiment feature extraction and confirmed that radical information could be adopted as a feature unit in sentiment analysis and that domain-dependent radicals could be reused in different corpora.

TL;DR: A new framework that directly addresses three possible errors in the vowel detection problem, namely vowel deletion, consonant insertion, and vowel insertion is proposed and outperforms the existing well-known methods in terms of both total error and F-measure.

TL;DR: This study investigated students' conversations with a virtual science tutor (Marni), either individually or in small groups, to see if students receiving tutoring using the virtual tutor in groups would demonstrate learning gains equivalent to those of students receiving one-on-one tutoring.

TL;DR: A multilinear statistical model of the human tongue that captures anatomical and tongue pose related shape variations separately is presented and it is shown that it can be used to generate plausible tongue animation by tracking sparse motion capture data.

TL;DR: An improved version of HM is introduced that leads to accurate and reliable estimation of voiced segments, fundamental frequency, HNR, jitter, and shimmer and the utility of developed measures on the speech-based assessment of cognitive impairments including clinical depression and autism spectrum disorder.

TL;DR: A new approach for transforming the traditional dialogue architecture into an incremental one at a low cost is presented: a new turn-taking decision module called the Scheduler is inserted between the Client and the Service.

TL;DR: Experimental studies on two different Indian languages suggest that CS/SR based footprint reduction methods can be used as an alternative to existing compression methods employed in USS system.

TL;DR: This work investigates on the application of cepstral distance as a distortion measure that turns out to be closely related to properties of the room acoustics, such as reverberation time and direct-to-reverberant ratio.