Abstract

Shouted speech signals have been studied mostlyfor utterances or word segments. In the production features derived over these segments, the differences between normal and shouted speech may sometimes be masked by variations due to pauses and unvoiced regions. Also, our recent study of electroglottograph signals has highlighted the usefulness of examining changes in the glottal excitation source characteristics during production of shouted speech. In this paper we examine changes in the shout production features in the vowel regions,that are found automatically by using the knowledge of accurate vowel onset points and epochs. Vowel regions are produced by periodic impulse-like excitation and contain relatively high signal energy. Hence the source features the instantaneous fundamental frequency and strength of excitation are examined, along with the filter feature the dominant frequency, for five different steady vowel regions. Changes in these features, that distinguish between normal and shouted speech, are more prominent in the automatically found vowel regions than for the entire word segments. This insight can help improving the automatic shout detection and other paralinguistic applications where acoustic cues need to be found in the vowel regions, e.g., detection of emotion categories.

Abstract

Automatic detection of shout prosody in continuous speech signal involves examining changes in its production characteristics. Our recent study of electroglottograph signals highlighted that significant changes occur in the glottal excitation source characteristics during production of shouted speech, especially in the vowel contexts. But the differences between normal and shouted speech, in the production features derived over utterances or word segments, may be masked sometimes by pauses or unvoiced regions related variations. Also, for such a real-time system, these vowel regions need to be found automatically. In this paper, changes in the shout production features are examined in the automatically detected vowel regions. Production of a vowel involves periodic impulse-like excitation and relatively high signal energy. Hence, the knowledge of epochs using zero-frequency filtering, and accurate vowel onset points can be used for detecting these regions. Changes in two excitation source features, the instantaneous fundamental frequency and strength of excitation, and in a vocal tract filter feature the dominant frequency, are examined for five steady vowel regions. Larger changes in these distinguishing features are observed in the automatically found vowel regions, than in word segments. This approach can help improving the systems for automatic detection of shout regions in continuous speech, and in paralinguistic applications that involve detection of prosody or emotions

Abstract

In this paper, we analyze the performance of various sequence to sequence neural networks on the task of grapheme to phoneme (G2P) conversion. G2P is a very important component in applications like text-to-speech, automatic speech recognition etc,. Because the number of graphemes that a word consists of and the corresponding number of phonemes are different, they are first aligned and then mapped. With the recent advent of sequence to sequence neural networks, the alignment step can be skipped allowing us to directly map the input and output sequences. Although the sequence to sequence neural nets have been applied for this task very recently, there are some questions concerning the architecture that need to be addressed. We show in this paper that, complex recurrent neural network units (like long-short term memory cells) may not be required to achieve good performance on this task. Instead simple recurrent neural networks (RNN) will suffice. We also show that the encoder can be a uni-directional RNN as opposed to the usually preferred bi-directional RNN. Further, our experiments reveal that encoder-decoder models with soft-alignment outperforms fixed vector context counterpart. The results demonstrate that with very few parameters we can indeed achieve comparable performance to much more complicated architectures.

Abstract

The main focus of this study is to analyze the performance of Automatic Speech Recognition (ASR) in different emotional environments using prosody modification. The majority of ASR systems are trained using neutral speech and the performance of such systems degrade when tested with the emotional speech. In this paper, the various components of speech that contribute to the emotion characteristics are studied. The prosody features of the source emotional utterancesare modified according to the target neutral utterances using Flexible Analysis Synthesis Tool (FAST). In the FAST, Dynamic Time Warping (DTW) is used to align the source emotional and target neutral utterances. Components of the prosody such as intonation, duration and excitation source are manipulated to incorporate the desired features into the source utterance. The modified (source emotional) utterances are then used for testing the ASR system which is trained using neutral speech. In this study, three emotions (compassion, happiness and anger) are considered for the analysis. Experimental results indicate an average improvement in the speech recognition system performance by considering prosody modified speech.

Abstract

In recent years, text-to-speech (TTS) systems have shown considerable improvement as far as the quality of the synthetic speech is concerned. Data driven synthesis methods using syllable as basic unit for concatenation, have proved to generate high quality speech for Indian Languages because of their advantage of prosodic matching function. However, still there is no acceptable solution to the optimal selection of speech segments in terms of audible discontinuities and human perception. This problem gets aggravated in the cases where there is no enough data for building the voice due to the missing units. In this paper, we continue our efforts in trying to address this by investigating the use of a new continuity measure based on maximum signal correlation for optimal selection of units in concatenative text-to-speech (TTS) synthesis framework. We explore two formulations for calculating the signal correlation: cross correlation (CC) based and average magnitude difference function (AMDF) based. We first perform an initial experiment to understand the significance of the approach and then build 5 experimental systems. Evaluations on 30 sentences for each of these languages by native users of the language show that the proposed continuity measure results in more natural sounding synthesis.

Abstract

Automatic detection of vowel landmarks is useful in many applications such as automatic speech recognition (ASR), audio search, syllabification of speech and expressive speech processing. In this paper, acoustic features extracted around epochs are proposed for detection of vowel landmarks in continuous speech. These features are based on zero frequency filtering (ZFF) and single frequency filtering (SFF) analyses of speech. Excitation source based features are extracted using ZFF method and vocal tract system based features are extracted using SFF method. Based on these features, a rule-based algorithm is developed for vowel landmark detection (VLD). Performance of the proposed VLD algorithm is studied on three different databases namely, TIMIT (read), NTIMIT (channel degraded) and Switchboard corpus (conversational speech). Results show that the proposed algorithm performs equally well compared to state-of-the-art techniques on TIMIT and betteron NTIMIT and Switchboard corpora. Proposed algorithmalso displays consistent performance on TIMIT and NTIMITdatasets for different levels of noise degradations.

Abstract

In natural conversations, significant part of laughter co-occurs with speech which is referred to as speech-laugh. Hence, speech-laugh will have characteristics of both laughter and neutral speech. But it is not clearly evident how acoustic properties of neutral speech are influenced by its co-occurring laughter. The objective of this study is to analyze the acoustic variations between vowel regions of laughter, speech-laugh and neutral speech. The features based on excitation source characteristics extracted at epochs are considered in this study. Features extracted in the vowel regions of speech-laugh exhibit deviations from that of laughter and neutral speech. These deviations in feature values are exploited to discriminate speech-laugh from laughter and neutral speech. Two different datasets consisting of conversational speech and meeting recordings are used in this analysis. Experimental results show that the discrimination between the three classes obtained by considering vowel regions is better than that of considering the complete utterance.

Abstract

The present study focuses on the text-independent speaker recognition in emotional conditions. In this paper, both system and source features are considered to represent speaker specific information. At the model level, Gaussian Mixture Models (GMMs), Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Deep Neural Networks (DNN) are explored. The experiments are performed using 3 emotional databases, i.e. German emotional speech database (EMO-DB), IITKGP-SESC: Hindi and IITKGP-SESC: Telugu databases. The emotions considered in the present study are neutral, anger, happy and sad. The results show that, the performance of a speaker recognition system trained with clean speech is degrading while testing with emotional data irrespective of feature used or model used to build the system. The best results are obtained for the score level fusion of system and source features based systems when speakers are modeled with DNNs.

Abstract

Due to increase of online product reviews posted daily through various modalities such as video, audio and text, sentimental analysis has gained huge attention. Recent developments in web technologies have also enabled the increase of web content in Hindi. In this paper, an approach to detect the sentiment of an online Hindi product reviews based on its multi-modality natures (audio and text) is presented. For each audio input, Mel Frequency Cepstral Coefficients (MFCC) features are extracted. These features are used to develop a sentiment models using Gaussian Mixture Models (GMM) and Deep Neural Network (DNN) classifiers. From results, it is observed that DNN classifier gives better results compare to GMM. Further textual features are extracted from the transcript of the audio input by using Doc2vec vectors. Support Vector Machine (SVM) classifier is used to develop a sentiment model using these textual features. From experimental results it is observed that combining both the audio and text features results in improvement in the performance for detecting the sentiment of an online product reviews.

Abstract

Source and system features are extensively used in building Speaker Recognition (SR) systems. In this paper, we investigate the influence of source and system features on the performance of the SR system in emotional conditions. The Linear Prediction Residual Cepstral Coefficients (LPRCC) which corresponds to source features, and Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) that correspond to system features are used for modeling SR system. A maximum-likelihood classifier based on Gaussian mixture density functions is used and experiments are carried out on 3 standard emotional speech databases (Indian Institute of Technology-Simulated Emotion speech corpus (IITKGP-SESC): Hindi, IITKGP-SESC: Telugu and German Emotional Speech Database (EMO-DB)). The speaker models are trained with neutral utterances and tested with 3 types (anger, happy and sad) of emotional speech utterances. Based on experimental results, the performance degradation was observed in mismatched (trained with one emotion and tested with other emotion) case and the average percentage of degradation in SR task using source features is approximately 16% more compared to system features. The performance degradation of SR system is nullified when trained and tested with the same emotion.