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

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

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

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

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

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

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

A dominating set of a small size is useful in several settings including wireless networks, document summarization, secure system design, and the like. In this paper, we start by studying three distributed algorithms that produce a small sized dominating sets in a few rounds. We interpret these algorithms in the natural shared memory setting and experiment with these algorithms on a multi-core CPU. Based on the observations from these experimental results, we propose variations to the three algorithms and also show how the proposed variations offer interesting trade-offs with respect to the size of the dominating set produced and the time taken.

Abstract

Recent developments in deep learning methods have greatly influenced the performances of speech recognition systems. In a Hidden Markov model-Deep neural network (HMM-DNN) based speech recognition system, DNNs have been employed to model senones (context dependent states of HMM),where HMMs capture the temporal relations among senones. Due to the use of more deeper networks significant improvement in the performances has been observed and developing deep learning methods to train more deeper architectures has gained a lot of scientific interest. Optimizing a deeper network is more complex ask than to optimize a less deeper network, but recently residual network have exhibited a capability to train a very deep neural network architectures and are not prone to vanishing/exploding gradient problems. In this work, the effectiveness of residual networks have been explored for of speech recognition. Along with the depth of the residual network, the criticality of width of the residual network has also been studied. It has been observe dthat at higher depth, width of the networks is also a crucial parameter for attaining significant improvements. A 14-hoursubset of WSJ corpus is used for training the speech recognition systems, it has been observed that the residual networks have shown much ease in convergence even with a depth much higher than the deep neural network. In this work, using residual networks an absolute reduction of 0.4 in WER error rates (8%reduction in the relative error) is attained compared to the best performing deep neural network.

Abstract

Significant developments in deep learning methods have been achieved with the capability to train more deeper networks. The performance of speech recognition system has been greatly improved by the use of deep learning techniques. Most of the developments in deep learning are associated with the development of new activation functions and the corresponding initializations. The development of Rectified linear units (ReLU)has revolutionized the use of supervised deep learning methods for speech recognition. Recently there has been a great deal of research interest in the development of activation functionsLeaky-ReLU (LReLU), Parametric-ReLU (PReLU), Exponentia lLinear units (ELU) and Parametric-ELU (PELU). This work isaimed at studying the influence of various activation functions onspeech recognition system. In this work, a hidden Markov model-Deep neural network (HMM-DNN) based speech recognitionis used, where deep neural networks with different activationfunctions have been employed to obtain the emission probabilitiesof hidden Markov model. In this work, two datasets i.e., TIMITand WSJ are employed to study the behavior of various speechrecognition systems with different sized datasets. During thestudy, it is observed that the performance of ReLU-networksis superior compared to the other networks for the smaller sizeddataset (i.e., TIMIT dataset). For the datasets of sufficientlylarger size (i.e., WSJ) performance of ELU-networks is superiorto the other networks.