Abstract

Identifying language information from speech utterance is referred to as spoken language identification. Language Identification (LID) is essential in multilingual speech systems. The performance of LID systems have been studied for various adverse conditions such as background noise, telephonic channel, short utterances, so on. In contrast to these studies, for the first time in the literature, the present work investigated the impact of emotional speech on language identification. In this work, different emotional speech databases have been pooled to create the experimental setup. Additionally, state-of-art i-vectors, timedelay neural networks, long short term memory, and deep neural network x-vector systems have been considered to build the LID systems. Performance of the LID system has been evaluated for speech utterances of different emotions in terms of equal error rate and Cavg. The results of the study indicate that the speech utterances of anger and happy emotions degrades performance of LID systems more compared to the neutral and sad emotions. Index Terms—Language Identification, i-vector, TDNN, LSTM, DNN x-vector

Abstract

The objectives of the dysarthria assessment are to discriminate dysarthric speech from normal speech, to estimate the severity of dysarthria in terms of the dysarthric speech intelligibility, and to find the motor speech subsystem which causes defects in speech production. In this work, analytic phase features are investigated for the objective assessment of dysarthria. In this connection, the importance of analytic phase in speech intelligibility is studied by employing phase modification schemes. The current investigation on the analytic phase, proposed a novel approach to estimate the instantaneous frequency components from the speech signal, by using single frequency filtering technique. In this study, dysarthric speech detection and intelligibility assessment systems are developed by using UA-Speech database. The efficiency of the analytic phase features is compared with state-of-the-art spectral features. The proposed features outperformed the magnitude and group delay features and shown a classification accuracies of 95.61% and 64.47% for dysarthric speech detection and intelligibility assessment tasks, respectively. The fusion of the evidence obtained from the analytic phase and magnitude spectral features revealed the complementary nature of analytic phase features.

Abstract

Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing methods explore the short-term spectral information from speech to assess hy-pernasality. The present work examines long-term average spectral (LTAS) features obtained from speech to detect and assess hyper-nasality. This work proposes single frequency filter bank based long-term average spectral (SFFB-LTAS) features for hypernasality detection and assessment. The SFFB is used to extract long-term average spectra with a good spectral resolution. The experiments are carried out using NMCPC-CLP database collected from 41 speakers with CLP and 32 speakers without CLP. The experimental results show that, SFFB-LTAS features performed better compared to state-of-art spectral and prosody features. The proposed systems for the detection and assessment of hypernasality have shown classification accuracy of 89% and 82.1%, respectively.

Abstract

The ASVspoof 2019 challenge focuses on countermeasures for all major spoofing attacks, namely speech synthesis (SS), voice conversion (VC), and replay spoofing attacks. This paper describes the IIIT-H spoofing countermeasures developed for ASVspoof 2019 challenge. In this study, three instantaneous cepstral features namely, single frequency cepstral coefficients, zero time windowing cepstral coefficients, and instantaneous frequency cepstral coefficients are used as front-end features. A Gaussian mixture model is used as back-end classifier. The experimental results on ASVspoof 2019 dataset reveal that the proposed instantaneous features are efficient in detecting VC and SS based attacks. In detecting replay attacks, proposed features are comparable with baseline systems. Further analysis is carried out using metadata to assess the impact of proposed countermeasures on different synthetic speech generating algorithm/replay configurations

Abstract

With the growing popularity of social networks, cloud services and online applications, people are becoming concerned about the way companies store their data and the ways in which the data can be applied. Privacy with devices and services operated by the voice are of particular interest. To enable studies in privacy, this paper presents a database which quantifies the experience of privacy users have in spoken communication. We focus on the effect of the acoustic environment on that perception of privacy. Speech signals are recorded in scenarios simulating real-life situations, where the acoustic environment has an effect on the experience of privacy. The acoustic data is complemented with measures of the speakers’ experience of privacy, recorded using a questionnaire. The presented corpus enables studies in how acoustic environments affect peoples’ experience of privacy, which in turn, can be used to develop speech operated applications which are respectful of their right to privacy. Index Terms: Experience of privacy, speech interfaces, speech corpus, acoustic environment, right to privacy

Abstract

Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. The recognition of these emotions in human speech is considered to be the crucial aspect of human-machine interaction. The combined spectral and differenced prosody features are considered for the task of the emotion recognition in the first stage. The task of emotion recognition does not serve the sole purpose of improvement in the performance of an ASR system. Based on the recognized emotions from the input speech, the corresponding adapted emotive ASR model is selected for the evaluation in the second stage. This adapted emotive ASR model is built using the existing neutral and synthetically generated emotive speech using prosody modification method. In this work, the importance of emotion recognition block at the front-end along with the emotive speech adaptation to the ASR system models were studied. The speech samples from IIIT-H Telugu speech corpus were considered for building the large vocabulary ASR systems. The emotional speech samples from IITKGP-SESC Telugu corpus were used for the evaluation. The adapted emotive speech models have yielded better performance over the existing neutral speech models.

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

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

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.