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Nanotechnology introduces revolutionary approaches for agriculture in the form of nano-based pesticides, fertilizers, sensors, weed-controlling agents, enhanced seed germination materials, etc. Even though metal-nanoparticles (NPs) have shown their potential to improve crop yield, the mode of action at the cellular level and fate in the human body and the environment are not well understood yet. Several metal-nanoparticles have been studied extensively by researchers for their active role in enhancing the rate of seed germination and crop quality augmentation which may happen due to several mechanisms such as increased porosity in nano-primed seeds inducing up-regulation of the expression of aquaporin and Reactive Oxygen Species (ROS) genes involved in water uptake, improving the root dehydrogenase activity to enhance the water absorption capability, etc. However, researchers have also demonstrated and reported the possible toxicity of NPs in the environment due to their agricultural practices. But the fate of NPs and their environmental impact are still unclear and largely vary based on several factors such as the size of NPs, coating material, mode of discharge and locations, etc. This review thoroughly focuses on the mode of action of various NPs in seed germination and accumulation, translocation through cells, and potential environmental and health risks.

Intent classification is a task in spoken language understanding. An intent classification system is usually implemented as a pipeline process, with a speech recognition module followed by text processing that classifies the intents. There are also studies of end-to-end system that take acoustic features as input and classifies the intents directly. Such systems don’t take advantage of relevant linguistic information, and suffer from limited training data. In this work, we propose a novel intent classification framework that employs acoustic features extracted from a pretrained speech recognition system and linguistic features learned from a pretrained language model. We use knowledge distillation technique to map the acoustic embeddings towards linguistic embeddings. We perform fusion of both acoustic and linguistic embeddings through cross-attention approach to classify intents. With the pro-posed method, we achieve 90.86% and 99.07% accuracy on ATIS and Fluent speech corpus, respectively.

This work explores the scope of duration modification for speaker verification (SV) under mismatch speech tempo condition. The SV performance is found to depend on speaking rate of a speaker. The mismatch in the speaking rate can degrade the performance of a system and is crucial from the perspective of deployable systems. In this work, an analysis of SV performance is carried out by varying the speaking rate of train and test speech. Based on the studies, a framework is proposed to compensate the mismatch in speech tempo. The framework changes the duration of test speech in terms of speaking rate according to the derived mismatch factor between train and test speech. This in turn matches speech tempo of the test speech to that of the claimed speaker model. The proposed approach is found to have significant impact on SV performance while comparing the performance under mismatch conditions. A set of practical data having mismatch in speech tempo is also used to cross-validate the framework.

The research in statistical parametric speech synthesis is towards improving naturalness and intelligibility. In this work, the deviation in spectral tilt of the natural and synthesized speech is analyzed and observed a large gap between the two. Furthermore, the same is analyzed for different classes of sounds, namely low-vowels, mid-vowels, high-vowels, semi-vowels, nasals, and found to be varying with category of sound units. Based on variation, a novel method for spectral tilt enhancement is proposed, where the amount of enhancement introduced is different for different classes of sound units. The proposed method yields improvement in terms of intelligibility, naturalness, and speaker similarity of the synthesized speech.

Lyrics-to-audio alignment is to automatically align the lyrical words with the mixed singing audio (singing voice+musical accompaniment). Such alignment can be achieved with an automatic speech recognition (ASR) system. We propose to adapt the acoustic model of a speech recognizer towards solo singing voice. This avoids the hurdles of annotating a large polyphonic music training dataset. Moreover, a lexicon-modification based duration modelling has been incorporated to account for the long duration vowels in singing. As practical application demand the alignment on polyphonic music, we study the effect of different singing vocal separation methods in the task of lyrics-to-audio alignment in polyphonic music. The extracted vocals are forced-aligned with the singing-adapted models. We demonstrate that the use of audio source separation method and effective end-pointing of the songs has a high impact on the alignment performance through the experiments. We report a mean average absolute error of 3.87 seconds, which is comparable with the state-of-the-art lyrics-to-audio alignment system that is trained on a large polyphonic music database.

The term polyglot speech synthesis refers to the process of producing speech in multiple languages and single speaker's voice from a single text-to-speech synthesis (TTS) system. This report reviews existing efforts in the literature to develop a polyglot TTS. Different methods described in this review mainly focus on developing a natural, intelligible, and cost-effective TTS system for multilingual text input. Since multilingual text is becoming very common in all applications of TTS, recent focus is made on developing a cost-effective polyglot TTS system, instead of conventional monolingual TTS. This review also discusses the pros and cons of different methods and mentions possible directions to overcome the limitations.

Accurate detection of pause boundary plays a major role in the segmentation of the speech corpus and improving the quality of speech synthesis. For pause modelling, we need to have pause tags in the training sentences. Manual tagging of pause is accurate but have the possibilities of missing out due to human error, and it is time-consuming. In this work, an automatic approach for marking the pause in the training corpus is proposed. During the training phase, after every word explicit pause (PAU) tags are added to represent a pause. Then, models for all phones including PAU are trained and re-alignment is performed. During re-alignment, each PAU boundary is corrected using three speech specific features namely, modulation spectrum energy, spectral peaks energy, and strength of excitation. The proposed approach gives a better result as compared to manual pause marking with less time complexity. It also improves the overall segmentation accuracy. The tagged label files are used for developing text-to-speech synthesis system using Hidden Markov Model based speech synthesis framework. Subjective evaluation is performed for various approaches used in tagging the pause. The experimental evaluation shows that accurate pause marking plays an important factor for improving the quality of synthesized speech in terms of naturalness and intelligibility.

The research in the area of text-to-speech synthesis is going forward to achieve more naturalness in the synthesized speech. Pause prediction from the text to be synthesized plays a vital role in achieving naturalness. From the perspective of speech production, some speech specific features before and after the pause may be coordinated with the pause prediction process. Based on this hypothesis, pattern of features, namely, the modulation spectrum energy, the strength of excitation, and peak-to-dip ratio from smoothed Hilbert envelope of linear prediction residual are analyzed relative to presence or absence of pause, at word junctures from manually pause marked database. While most of the existing works rely only on linguistic aspects for predicting pause position, in this work, support vector machines (SVMs) are trained using both speech based features and linguistic features to predict position of pause at word junctures. The accuracy of pause prediction method is improved to 96.57% by adding speech based evidences, while the prediction accuracy is 90.07% when only semantic features are used. The same SVM classifier is used for pause insertion in the synthesized speech. For this initially speech is synthesized without any pause prediction, from which signal based features are derived at each word juncture. Based on the previously trained classifier output for these features, pauses are inserted in the synthesized speech. Subjective evaluation shows improvement in naturalness and intelligibility of synthesized speech after using proposed pause insertion method.

The pivotal role played by arbuscular mycorrhizal fungi (AMF) in maintaining plant and soil health has made this group of beneficial fungi a star celebrity amongst a host of beneficial microbes used by human brain for its welfare. Now days because of their myriad beneficiary effects especially on the plant health, they are considered as one of the most potent biological tools for restoration purposes. This review hence focused on the role played by AMF on eco restoration, reaction of AMF to their hosts and the myriad challenges faced by different workers for mycorrhizal application in degraded soils.

Grapheme to phoneme conversion modules are essential components in text-to-speech (TTS) systems. These modules operate before the phone sequence is fed into the synthesis routine. However, additional challenges emerge when such conversion modules are implemented with non-native varieties of languages, such as Indian English. As many of the the existing grapheme to phoneme dictionaries represent American English pronunciation, they are not suitable for use in Indian English TTS systems. Hence, in this work, an effort has been made to modify the existing English grapheme to phoneme dictionary by implementing specific rules for one particular variety of Indian English, namely Assamese English. The proposed method of dictionary modification is applied at the front end of the Indian English TTS, developed using unit selection synthesis and statistical parametric speech synthesis frameworks. In both frameworks, significant improvement is achieved in subjective evaluation when the dictionary is adapted to Assamese English pronunciation. The word error rate decreased from 46.67% to 7.69% after incorporating the variety specific modifications to the dictionary, indicating significant perceptual improvement.

A Unit Selection based Synthesis (USS) system selects appropriate units from the inventory based on target cost and join cost. For a syllable based USS system, it is not practically feasible to cover all possible syllables in a language with all possible contexts. Hence, some discontinuity is observed at the concatenation points in the synthesized speech due to unavailability of units with proper target specifications in the inventory. In this work, we are using prosody modification of the syllables before concatenation to match fundamental frequency (F0) and intensity of adjacent syllables. This approach is also helpful to build a small footprint syllable level USS system, where many examples of the basic units with varying prosodical and spectral features is not available. The discontinuity in pitch and intensity at the concatenation point is reduced after prosody modification. Moreover, naturalness and intelligibility of synthesized speech is found to be improved using the proposed method.

This paper presents the design and development of Assamese Text to speech (TTS) synthesis system. In particular, work focused on designing language specific rules, developing quality database, data segmentation, and to handle bilingual sound units. In Assamese language, till now no study is done to construct the grapheme to phoneme conversion rules. In this work, grapheme to phoneme conversion rules are proposed for Assamese language. The database is recorded by checking the speaking rate, variation in amplitude level, dc wandering, and clipping during data collection. A significant improvement in the synthesized voice is observed by ensuring uniform speaking rate, controlling variation in the signal amplitude level, and avoiding dc wandering and clipping during data collection. A semi-automatic segmentation approach is developed for data segmentation. Initially, segmentation is done by automatic process and later manual correction of segmentation boundaries is done to improve quality and intelligibility. It also reduce time required for the segmentation process. The developed TTS can work in bilingual mode. It can switch between Assamese and English language smoothly and maintains the sentence level intonation even for mixed texts.

Automatic segmentation of speech using embedded reestimation of monophone hidden Markov models (HMMs) followed by forced alignment may not give accurate boundaries. Group delay (GD) processing for refining the boundaries at the syllable level is attempted earlier. This paper aims at exploring vowel onset point (VOP) and vowel offset or end point (VEP) for correcting the boundaries obtained using HMM alignment. HMM models the class information well, however may not detect the exact boundary. In case of VOPs and VEPs, spurious rate or miss rate can be there, but detected boundaries are more accurate. Combining both HMM and VOP/VEP gives improvement in terms of log likelihood scores of forced aligned phoneme boundaries. HMM boundaries are corrected using VOP/VEP and model parameters are reestimated at the syllable level. Results are compared with that of GD based correction and found that overall performance is comparable. Performance for vowels is found to be higher than that of GD based refinement as the refinement in this case is mainly at the vowel boundaries. HMM based speech synthesis systems (HTS) are developed using phone as a basic unit with the proposed segmentation method. Subjective evaluation indicates that there is an improvement in the quality of synthesis.

The objective of this work is to propose a simple and faster method for prosody modification that avoids complex procedure of deriving the modified epoch sequence. The proposed method processes the speech signal by zero frequency filtering (ZFF) to extract epochs. The extracted epochs are time scaled using the knowledge of given prosody modification factor. The prosody modified (PM) speech is obtained by copying the speech signal samples around the original epochs using the knowledge of time scaled epochs. The computational advantage of the proposed method is due to not deriving any modified pitch markers. The time scaled epochs act only as guiding sequence for the construction of prosody modified speech. The experimental results of the proposed method are compared with earlier epoch based prosody modification methods and found to provide same perceptual quality, but with a faster process.

Soil is considered as one of the richest source of microbes on the earth. Understanding microbial diversity of soil is a daunting and challenging task. The culture-independent techniques have revealed that the soil microbial diversity is much larger than was understood through culture-dependent techniques. Various DNA- and RNA-based techniques have revolutionized our understanding of soil microbial diversity. This chapter deals with the major culture-independent methods used for the study of soil microbial diversity, their advantages and limitations and advent of metagenomics in microbial diversity studies. Significance of culture-independent techniques in conservation of microbes has also been discussed.