Abstract

The easy implementation of replay attacks by a fraudster poses a severe threat to automatic speaker verification (ASV) technology than the other spoofing attacks like speech synthesis and voice conversion. Replay attacks refer to an attack by a fraudster to get illegitimate access to an ASV system by playing back the speech sample collected from genuine target speaker. The significant cues that can differentiate between genuine and replay recordings are channel characteristics. To capture these characteristics, one need to extract features from the spectrum, which should have high spectral and temporal resolutions. Zero time windowing (ZTW) analysis of speech is one such time-frequency analysis technique, which results in high spectral and temporal resolution spectrum at each sampling instant. In this study, new features are proposed by applying cepstral analysis to ZTW spectrum. Experiments are performed on two publicly available replay attack databases namely BTAS 2016 and ASVspoof 2017. The first set of experiments are conducted using Gaussian mixture models to evaluate the potential of proposed features. Performance of the proposed system in terms of half total error rate is 0.75% and in terms of equal error rate is 14.75% on BTAS 2016 and ASVspoof 2017 evaluation sets respectively. A score level fusion is performed by using proposed features with previously proposed single frequency filtering cepstral coefficients. This fused result outperformed the previously reported best results on these two datasets

Abstract

In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifier using an expectation maximization algorithm. We derive the generalization bounds of the proposed approach. Through extensive simulation study, we show that the proposed method performs comparably to other state-of-the-art approaches.

Abstract

This paper proposes a new speech feature representation that improves the intelligibility assessment of dysarthric speech. The formulation of the feature set is motivated from the human auditory perception and high time-frequency resolution property of single frequency filtering (SFF) technique. The proposed features are named as perceptually enhanced single frequency cepstral coefficients (PESFCC). As a part of SFF technique implementation, speech signal passed through a single pole complex bandpass filter bank to obtain high-resolution time-frequency distribution. Then, the distribution is enhanced by using a set of auditory perceptual operators. Lastly, traditional homomorphic analysis has been carried out on the resulting signal to obtain PE-SFCC feature vector. The performance of proposed features in dysarthric speech detection and its intelligibility assessment has been reported on UASPEECH database. The PE-SFCC features outperformed the state-of-the-art features in dysarthric speech detection and intelligibility assessment.

Abstract

In this paper, we propose an approach for learning sparse reject option classifiers using double ramp loss Ldr. We use DC programming to find the risk minimizer. The algorithm solves a sequence of linear programs to learn the reject option classifier. We show that the loss Ldr is Fisher consistent. We also show that the excess risk of loss Ld is upper bounded by excess risk of Ldr. We derive the generalization error bounds for the proposed approach. We show the effectiveness of the proposed approach by experimenting it on several real world datasets. The proposed approach not only performs comparable to the state of the art, it also successfully learns sparse classifiers.

Abstract

fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Stateof-the-art encoding models use a single global model (linear or non-linear) to predict brain activation (all the voxels) given the stimulus. However, the critical assumption in these methods is that a priori different brain regions respond the same way to all the stimuli, that is, there is no modularity or specialization assumed for any region. This goes against the modularity theory, supported by many cognitive neuroscience investigations suggesting that there are functionally specialized regions in the brain. In this paper we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model. The key idea here is that each linear expert captures the behaviour of similar brain regions. Given a new stimulus, the utility of the proposed model is twofold (i) predicts the brain activation as a weighted linear combination of the activations of multiple linear experts and (ii) to learn multiple experts corresponding to different brain regions. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. This study helps in understanding the brain regions that are activated together given different kinds of stimuli. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration. Experiments on fMRI data from paradigm 1 [1] where participants view linguistic stimuli show that the proposed MoRE model has better prediction accuracy compared to that of conventional models. Our model achieves a mean absolute error (MAE) of 3.94, with an R 2-score of 0.45 on this data set. This is an improvement over performance of traditional methods including, ridge regression (5.58 MAE, 0.15 R 2 -score), MLP (4.63 MAE, 0.35 R 2 -score). We also elaborate on the specializations captured by various experts in our mixture model and their implications.

Abstract

Epochs are the abrupt-closure events in vocal fold vibration during the production of voiced speech. Zero frequency filtering is a simple and effective technique used to estimate the glottal closure instants accurately from the speech signal. However, the zero frequency filter is an unstable system. Hence, it may not be suitable for practical implementation due to the requirements of high precision computation. In this letter, zero-phase zero frequency resonator is proposed as an alternative to zero frequency filter. The proposed approach provides a stable zero-phase response. The experimental results indicate that the performance of the proposed method outperformed the state-of-the-art methods in terms of identification rate 99.17% and provides comparable performance in terms of false alarm rate (0.41%), and identification accuracy (0.28 ms).

Abstract

Self-attention networks are being popularly employed in sequence classification and sequence summarization tasks. State-of-the-art models use sequential models to capture the high-level information, but these models are sensitive to length of utterance and do not equally generalize over variable length utterances. This work explores to study the efficiency of recent advancements in self-attentive networks for improving the performance of the LID system. In self-attentive network, variable length input sequence is converted to fixed dimensional vector which represents the whole sequence. The weighted mean of input sequence is considered as utterance level representation. Along with the mean, a standard deviation is employed to represent the whole input sequence. Experiments are performed using AP17-OLR database. Use of mean with standard deviation has reduced the equal error rate (EER) with an 8% relative improvement. A multi-head attention mechanism is introduced in self-attention networks with an assumption that each head captures the distinct information to discriminate languages. Use of multi-head self-attention has further reduced the EER with a 13% relative improvement. Best performance is achieved with multi-head self-attention network with residual connections. Shifted delta cepstral features (SDC) and stacked SDC features are used for developing LID systems.

Abstract

—India is a multilingual society having more than 1600 languages. Most of these languages are having an overlapping set of phonemes. This makes developing language identification (LID) framework difficult for Indian languages. In this paper, the above challenge is addressed using phonetic features. To model the temporal variations in phonetic features, attention based residual-time delay neural network (RES-TDNN) is proposed. This network effectively captures long-range temporal dependencies through TDNN and attention mechanism. The proposed network has been evaluated on IIITH-ILSC database using phonetic and acoustic features. The database consists of 22 official Indian languages and Indian English. Attention based RES-TDNN outperformed the other state-of-the-art networks such as deep neural network, long short-term memory network and produced an equal error rate of 9.46%. Further, the fusion of shifted delta cepstral and phonetic features have improved the performance.

Abstract

—India is a multilingual society having more than 1600 languages. Most of these languages are having an overlapping set of phonemes. This makes developing language identification (LID) framework difficult for Indian languages. In this paper, the above challenge is addressed using phonetic features. To model the temporal variations in phonetic features, attention based residual-time delay neural network (RES-TDNN) is proposed. This network effectively captures long-range temporal dependencies through TDNN and attention mechanism. The proposed network has been evaluated on IIITH-ILSC database using phonetic and acoustic features. The database consists of 22 official Indian languages and Indian English. Attention based RES-TDNN outperformed the other state-of-the-art networks such as deep neural network, long short-term memory network and produced an equal error rate of 9.46%. Further, the fusion of shifted delta cepstral and phonetic features have improved the performance.

Abstract

In this paper, phonetic features derived from the joint acoustic model (JAM) of a multilingual end to end automatic speech recognition system are proposed for Indian language identification (LID). These features utilize contextual information learned by the JAM through long short-term memoryconnectionist temporal classification (LSTM-CTC) framework. Hence, these features are referred to as CTC features. A multi-head self-attention network is trained using these features, which aggregates the frame-level features by selecting prominent frames through a parametrized attention layer. The proposed features have been tested on IIITH-ILSC database that consists of 22 official Indian languages and Indian English. Experimental results demonstrate that CTC features outperformed i-vector and phonetic temporal neural LID systems and produced an 8.70% equal error rate. The fusion of shifted delta cepstral and CTC feature-based LID systems at the model level and feature level further improved the performance.