Multi-Feature Integration for Speaker Embedding Extraction Sankala, Sreekanth; Rafi B, Shaik Mohammad; K, Sri Rama Murty
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2022-May-23
Conference Proceeding
The performance of the automatic speaker recognition system is becoming more and more accurate, with the advancement in deep learning methods. However, current speaker recognition system performances ...are subjective to the training conditions, thereby decreasing the performance drastically even on slightly varied test data. A lot of methods such as using various data augmentation structures, various loss functions, and integrating multiple features systems have been proposed and shown a performance improvement. This work focuses on integrating multiple features to improve speaker verification performance. Speaker information is commonly represented in the different kinds of features, where the redundant and irrelevant information such as noise and channel information will affect the dimensions of different features in a different manner. In this work, we intend to maximize the speaker information by reconstructing the extracted speaker information in one feature from the other features while at the same time minimizing the irrelevant information. The experiments with the multi-feature integration model demonstrate improved performance than the stand-alone models by significant margins. Also, the extracted speaker embeddings are found to be noise-robust.
“Malignant mesothelioma (MM)” is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a ...carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is “malignant mesothelioma.” Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination “epithelial/mesenchymal appearance of MM,” however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful “Malignant Mesothelioma illness” identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support “Vector Machine, Neural Network, and Decision Tree” are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.
In this paper, the importance of analytic phase of the speech signal in automatic speaker verification systems is demonstrated in the context of replay spoof attacks. In order to accurately detect ...the replay spoof attacks, effective feature representations of speech signals are required to capture the distortion introduced due to the intermediate playback/recording devices, which is convolutive in nature. Since the convolutional distortion in time-domain translates to additive distortion in the phase-domain, we propose to use IFCC features extracted from the analytic phase of the speech signal. The IFCC features contain information from both clean speech and distortion components. The clean speech component has to be subtracted in order to highlight the distortion component introduced by the playback/recording devices. In this work, a dictionary learned from the IFCCs extracted from clean speech data is used to remove the clean speech component. The residual distortion component is used as a feature to build binary classifier for replay spoof detection. The proposed phase-based features delivered a 9% absolute improvement over the baseline system built using magnitude-based CQCC features.
Globally, food waste (FW) is found to be one of the major constituents creating several hurdles in waste management. On the other hand, the energy crisis is increasing and the limited fossil fuel ...resources available are not sufficient for energy needed for emerging population. In this context, biohydrogen production approach through valorization of FW is emerging as one of the sustainable and eco-friendly options. The present review explores FW sources, characteristics, and dark fermentative production of hydrogen along with its efficiency. FW are highly biodegradable and rich in carbohydrates which can be efficiently utilized by anaerobic bacteria. Based on the composition of FW, several pretreatment methods can be adapted to improve the bioavailability of the organics. By-products of dark fermentation are organic acids that can be integrated with several secondary bioprocesses. The versatility of secondary products is ranging from energy generation to biochemicals production. Integrated approaches facilitate in enhanced energy harvesting along with extended wastewater treatment. The review also discusses various parameters like pH, temperature, hydraulic retention time and nutrient supplementation to enhance the process efficiency of biohydrogen production. The application of solid-state fermentation (SSF) in dark fermentation improves the process efficiency. Dark fermentation as the key process for valorization and additional energy generating process can make FW the most suitable substrate for circular economy and waste based biorefinery.
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•Carbohydrate rich food waste (FW) has high potential for biohydrogen production.•Biohydrogen production from FW treatment also addresses waste management.•Biorefineries and circular economy can be developed through FW valorization.•Lab scale to pilot scale experiences with dark fermentation were documented.
Automatic speaker verification (ASV) is the task of authenticating claimed identity of a speaker from his/her voice characteristics. State-of-the-art ASV systems rely on capturing the voice signature ...of a speaker in a fixed-dimensional embedding. Recent studies reported that the performance of the ASV system improves when phonetic information obtained from a phoneme recognizer is appended to the frame-level speech representations. This work aims at analyzing the relative significance of various phonetic classes in extracting the speaker discriminative embeddings. We use the temporal attention mechanism to analyze the importance of different phonetic classes in speaker verification. It is observed that vowels, fricatives, and nasals receive relatively higher attention in the speaker verification task. This observation is in accordance with the subjective studies reported earlier, which signify the speaker discriminative characteristics of vowels and nasals. In the process, we demonstrate the efficiency of self-supervised phonetic information in extracting robust speaker embeddings. The proposed self-supervised phonetic attentive ASV system achieved a relative improvement of 29.2% over the baseline x-vector system and 19.3% over its supervised counterpart.
This project aims to develop and demonstrate a ground robot with intelligence capable of conducting semi-autonomous farm operations for different low-heights vegetable crops referred as Agriculture ...Application Robot(AAR). AAR is a lightweight, solar-electric powered robot that uses intelligent perception for conducting detection and classification of plants and their characteristics. The system also has a robotic arm for the autonomous weed cutting process. The robot can deliver fertilizer spraying, insecticide, herbicide, and other fluids to the targets such as crops, weeds, and other pests. Besides, it provides information for future research into higher-level tasks such as yield estimation, crop, and soil health monitoring. We present the design of robot and the associated experiments which show the promising results in real world environments.
Software reliability is one of the important factors of software quality. Many mathematical models are proposed in literature to predict the software quality and related reliability. Generally during ...testing many factors are considered like effort, time and resources. Testing effort can be better described by time, person hours and number of test cases. During testing many resources are being consumed. In this paper an analysis is done based on incorporating the Bass diffusion testing-effort function in to NHPP Software reliability growth model and also observed its release policy. Experiments are performed on the real datasets. Parameters are calculated and observed that our model is best fitted for the datasets.
Network and data security in corporate organizations face immense threats due to increasing online vulnerabilities. Intrusion Detection Systems (IDSs) play a vital role in combating these threats, ...yet their effectiveness is often compromised by a high incidence of false alarms. To mitigate this issue, we propose a novel intrusion detection method, based on Parallel Long-Short-Term Memory (p-LSTM). This approach harnesses the power of parallelism to process network data more efficiently and accurately. Experimental outcomes demonstrate that our p-LSTM method significantly decreases false alarm rates, enhancing the reliability of intrusion detection. Moreover, it outperforms traditional LSTM and other cutting-edge algorithms in terms of specificity, recall, and F-score, thus underscoring its superiority. The successful implementation of our p-LSTM model signals a leap forward in the evolution of IDSs, promising better protection for corporate networks against external threats.
Reliability is one of the important quality measures of software product. Testing is one important phase in software development life cycle whose intention is to find the bugs present in the software ...product. A mathematical model which describes the software testing in software development cycle is termed as software reliability growth model. Past few decades many software reliability growth models were proposed. Estimating the accurate release time of software is an important challenging issue of software Development Company. If the software product is released early, it contains more errors and makes software to be less reliable, whereas late release will increase the development cost. It is observed that more than half of the total development cost is concentrated during the maintenance phase. Estimating the reliability and accurate development cost of software product by software reliability growth model at maintenance phase is an important issue. Warranty is defined to be an agreement between the customer and vendor of the software to provide the extra protection to the product. Several papers have concentrated on optimal release time by considering the warranty cost. All early proposed NHPP warranty cost models considered a perfect-debugging software reliability growth model while estimating the software maintenance cost. But it was observed that, the software product is influenced by several factors like environment, resources and nature of the faults during the operational phase. In such circumstances it is quite not right to consider a perfect NHPP software reliability growth model in operational phase. In this paper the authors propose an imperfect debugging software reliability growth model by combining the cost and reliability at a given warranty period to estimate the optimal release time of software product.
Software come to be an important element in recent times, from small residence hold gadgets to large machinery wishes fine software. software development is a technical oriented system where range of ...quantitative and qualitative duties have been completed parallel a good way to meets the needs of the consumer. Many people play a vital role within the improvement of software program product, consequently there is chance of committing errors by way of these humans and these errors becomes faults in later stages. Computing software program cost for the duration of software development can facilitate us predicting the time of release of the software. In this paper we have investigated release time of software program by way of considering the imperfect debugging software program reliability growth model and cost model.
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