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

A degradation in the performance of automatic speech recognition systems (ASR) is observed in mismatched training and testing conditions. One of the reasons for this degradation is due to the presence of emotions in the speech. The main objective of this work is to improve the performance of ASR in the presence of emotional conditions using prosody modification. The influence of different emotions on the prosody parameters is exploited in this work. Emotion conversion methods are employed to generate the word level non-uniform prosody modified speech. Modification factors for prosodic components such as pitch, duration and energy are used. The prosody modification is done in two ways. Firstly, emotion conversion is done at the testing stage to generate the neutral speech from the emotional speech. Secondly, the ASR is trained with the generated emotional speech from the neutral speech. In this work, the presence of emotions in speech is studied for the Telugu ASR systems. A new database of IIIT-H Telugu speech corpus is collected to build the large vocabulary neutral Telugu speech ASR system. The emotional speech samples from IITKGP-SESC Telugu corpus are used for testing it. The emotions of anger, happiness and compassion are considered during the evaluation. An improvement in the performance of ASR systems is observed in the prosody modified speech.

Abstract

In this paper, a combination of excitation source information and vocal tract system information is explored for the task of language identification (LID). The excitation source information is represented by features extracted from linear prediction (LP) residual signal called the residual cepstral coefficients (RCC). Vocal tract system information is represented by the mel frequency cepstral coefficients (MFCC). In order to incorporate additional temporal information, shifted delta cepstra (SDC) are computed. An LID system is built using SDC over both MFCC and RCC features individually and evaluated based on their equal error rate (EER). Experiments have been performed on a dataset consisting of 13 Indian languages with about 115 h for training and 30 h for testing using a deep neural network (DNN), DNN with attention (DNN-WA) and a state-ofthe-art i-vector system. DNN-WA outperforms the baseline i-vector system. An EER of 9.93 and 6.25% are achieved using RCC and MFCC features respectively. By combining evidence from both features using a late fusion mechanism, an EER of 5.76% is obtained. This result indicates the complementary nature of the excitation source information to that of the widely used vocal tract system information for the task of LID.

Abstract

Speaker normalization is one of the crucial aspects of an Automatic speech recognition system (ASR). Speaker normalization is employed to reduce the performance drop in ASR due to speaker variabilities. Traditional speaker normalization methods are mostly linear transforms over the input data estimated per speaker, such transforms would be efficient with sufficient data. In practical scenarios, only a single utterance from the test speaker is accessible. The present study explores speaker normalization methods for end-to-end speech recognition systems that could efficiently be performed even when single utterance from the unseen speaker is available. In this work, it is hypothesized that by suitably providing information about the speaker’s identity while training an end-to-end neural network, the capability to normalize the speaker variability could be incorporated into an ASR system. The efficiency of these normalization methods depends on the representation used for unseen speakers. In this work, the identity of the training speaker is represented in two different ways viz. i) by using a one-hot speaker code, ii) a weighted combination of all the training speakers identities. The unseen speakers from the test set are represented using a weighted combination of training speakers representations. Both the approaches have reduced the word error rate (WER) by 0.6, 1.3% WSJ corpus

Abstract

t Phonetic feature extraction is used in many types of speech applications. In this paper, we propose an approach for automatic detection of retroflex approximant /õ/ in a Tamil continuous speech using sonorant regions and formant structure of speech. Retroflex approximant is one of the distinctive phonetic features of Tamil speech. It occurs in V_V and V_ contexts. In the proposed approach, slopes of the formant trajectories and spectral dynamics of F2 and F3 in the region of retroflex approximant are considered as acoustic cues for detecting them in continuous speech. In this work, numerator of the group delay function-based formant extraction method is used to track formant trajectories. Based on experimental results, formants extracted using this approach produced better evidence compared to state-of-the-art formant extraction techniques for retroflex approximant detection.

Abstract

In this paper, a novel technique has been proposed for the vowel region detection from the continuous speech using an envelope of the derivative of the speech signal, which is a non-negative, frequency-weighted energy operator. The proposed vowel region detection method is implemented using a two-stage algorithm. The first stage of vowel region detection consists of speech signal analysis to detect vowel onset points (VOP) and vowel end-points (VEP) using an instantaneous energy contour obtained from the envelope of the derivative of a speech signal. The VOPs and VEPs are spotted using the peak-finding algorithm based upon the first order Gaussian differentiator. The next stage consists of removal of spurious vowel regions and the correction of hypothesized VOP and VEP locations using combined cues obtained from the uniformity of epoch intervals and strength of the excitation of the speech signal. Performance of the proposed method for detecting vowel regions from the speech signal is evaluated using TIMIT acoustic-phonetic speech corpus. The proposed approach resulted in significantly high detection rate and less false alarm rate compared to the state-of-the-art methods in both clean and noisy environments

Abstract

In this study, a robust language identification (LID) system is investigated with vowel region detection scheme incorporated at the front-end. The background noise is known to severely reduce language identification performance. In this work, Mel-frequency cepstral features extracted from the vowel regions instead of voiced speech are utilized for language identification system modeling. Experimentation is done using different vowel region detection algorithms on deep neural networks (DNN) and deep neural networks with attention (DNN-WA) based LID systems on IIITH Indian language dataset. Experimental results show that the accurate vowel region detection mechanism at the front-end of a LID system improved the performance in noisy environment.

Abstract

The physiological constraints in the human speech production mechanism restrict the emotional state of the speaker for a short duration in an emotional speech. In this work, the emotionally stressed regions are detected using the strength of the excitation (SoE) and fundamental frequency (F0) of the speech signal. These emotionally stressed regions for the different emotions are further classified using a linear kernel support vector machine (SVM) with a twostage binary decision logic. This classifier is modelled using the differenced prosody features extracted by considering the relative difference of the prosody components between normal and emotionally stressed regions. This experimentation is carried out using Berlin emotion speech (EMO-DB) database. The proposed approach produced a better result compared to existing state-of-the-art emotion recognition systems. An accuracy of 80.83% is reported in this paper.

Abstract

Vowels are produced with an open configuration of the vocal tract, without any audible friction. The acoustic signal is relatively loud with varying strength of impulselike excitation. Vowels possess significant energy content in the low-frequency bands of the speech signal. Acoustic events such as vowel onset point (VOP) and vowel end-point (VEP) can be used as landmarks to detect vowel regions in a speech signal. In this paper, two-stage algorithm is proposed to detect precise vowel regions. In the first level, the speech signal is processed using zero frequency filtering to emphasize energy content in low-frequency bands of speech. Zero frequency filtered signal predominantly contains lowfrequency content of the speech signal as it is filtered around 0 Hz. This process is followed by the extraction of dominant spectral peaks from the magnitude spectrum around glottal closure regions of the speech signal. The vowel onset points and vowel end-points are obtained by convolving the enhanced spectral contour of zero frequency filtered signal with first order Gaussian differentiator. In the next level, a post-processing is carried out in the regions around VOP and VEP to remove spurious vowel regions based on uniformity of epoch intervals. In addition, the positions of VOPs and VEPs are also corrected using the strength of the excitation of the speech signal. The performance of the proposed vowel region detection method is compared with the existing state of art methods on TIMIT acoustic-phonetic speech corpus. It is reported that this method produced significant improvement in vowel region detection in clean and noisy environments.

Abstract

Automatic language identification (LID) in practical environments is gaining a lot of scientific attention due to rapid developments in multilingual speech processing applications. When an LID is operated in noisy environments a degradation in the performance can be observed and it can be majorly attributed to mismatch between the training and operating environments. This work is aimed towards developing an LID system that can robustly operate in clean and noisy environments. Traditionally, to reduce the mismatch between training and operating environments, noise is synthetically induced to the training corpus and these models are termed as multi-SNR models. In this work, various curriculum learning strategies are explored to train multi-SNR models, such that the trained models have better generalization in performance over varying background environments. I-vector, Deep neural networks (DNN) and DNN With Attention (DNN-WA) architectures are used in this work for developing LID systems. Experimental verification of the proposed approach is carried out using IIIT-H Indian database and AP17-OLR database. The performance of LID system is tested at different signal-to-noise ratio (SNR) levels using white and vehicular noises from NOISEX dataset. In comparison to multi-SNR models, the LID systems trained with curriculum learning have performed better in terms of equal error rate (EER) and generalization in EER across varying background environments. The degradation in the performance of LID systems due to environmental noise has been effectively reduced by training multi-SNR models using curriculum learning.