Abstract

In this paper, a new method to detect disfluencies directly from speech is explored. The method makes use of pre-linguistic automatic syllabification - the process of segmenting input speech signals into perceptually distinct syllable-like regions, to develop syllable-level disfluency detection systems. Statistical prosody features related to fundamental frequency, energy and duration are extracted from each syllable-like region and used to train a DNN classifier for automatic detection of speech disfluencies. Further, complementary information useful for the task of disfluency detection is added to the pipeline with the help of acoustic features. A BiLSTM feature extractor is used to get complex acoustic representation from the baseline MFCC features for each syllable-like region. This acoustic representation is concatenated with the prosody features and used in the proposed system for detecting multiple speech disfluencies. Experiments are conducted for four types of disfluencies in the UCLASS and the IIITH-IED datasets to test the proposed disfluency detection system. Overall, it is found that the proposed system gives a detection accuracy of 88.75% for the disfluencies in the UCLASS dataset, whereas for the IIITH-IED dataset, the accuracy obtained is 91.24%, showing the effectiveness of considering perceptually distinct syllable-like regions as representational units for detecting disfluencies.

Abstract

An Internet of Things (IoT)-based healthcare monitoring system is an amalgamation of smart healthcare devices and applications which connect to the healthcare information system through the internet. It has opened up a world of possibilities in the medical field. Connected smart healthcare devices can collect important healthcare data and provide extra details about the symptoms. That further enables remote care as the patient can consult the appropriate healthcare expert. It generally provides patients more control over their lives and treatment. Moreover, a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS) is one step ahead as it can reduce unnecessary hospital visits and the burden on healthcare systems by connecting patients to their healthcare experts and allowing the secure transfer and storage of healthcare data by making use of the blockchain mechanism. Some of the possible applications of BIoT-HMS are “remote health monitoring,” “management of healthcare operations,” “detection and prevention of diseases” and many more. However, IoT-based healthcare monitoring systems have issues related to security breaches and privacy of healthcare data. Therefore, it is essential to design some security protocols for IoT-based healthcare monitoring systems. From time to time researchers propose different protocols to secure the data of IoT-based healthcare monitoring systems. However, most of them are vulnerable to various types of attack like, replay, MiTM, impersonation, secret credentials leakage, unauthorized data update, etc., Therefore, researchers came up with the idea of inclusion of blockchain-enabled security in the IoT-based healthcare monitoring system, which further can be considered as a blockchain-enabled IoT-based healthcare monitoring system (BIoT-HMS). In this chapter, we provide the details of various possible architectures of BIoT-HMS. We also provide the details of various threats and attacks along with the threat model in BIoT-HMS. The security requirements and applications of BIoT-HMS are also discussed. We then provide the summary of various existing security protocols related to BIoT-HMS. The comparison of “security and functionality features” of security protocols related to BIoT-HMS is also provided.

Abstract

We propose a pitch stylization technique in the presence of pitch halving and doubling errors. The technique uses an optimization criterion based on a minimum mean absolute error to make the stylization robust to such pitch estimation errors, particularly under noisy conditions. We obtain segments for the stylization automatically using dynamic programming. Experiments are performed at the frame level and the syllable level. At the frame level, the closeness of stylized pitch is analyzed with the ground truth pitch, which is obtained using a laryngograph signal, considering root mean square error (RMSE) measure. At the syllable level, the effectiveness of perceptual relevant embeddings in the stylized pitch is analyzed by estimating syllabic tones and comparing those with manual tone markings using the Levenshtein distance measure. The proposed approach performs better than a minimum mean squared error criterion based pitch stylization scheme at the frame level and a knowledgebased tone estimation scheme at the syllable level under clean and 20dB, 10dB and 0dB SNR conditions with five noises and four pitch estimation techniques. Among all the combinations of SNR, noise and pitch estimation techniques, the highest absolute RMSE and mean distance improvements are found to be 6.49Hz and 0.23, respectively. Index Terms: Pitch stylization, minimum MAE criterion, dynamic programming based segmentation, noise robustness

Abstract

—Automatic phrase boundary detection could be useful in applications, including computer-assisted pronunciation tutoring, spoken language understanding, and automatic speech recognition. In this work, we consider the problem of phrase boundary detection on English utterances spoken by native American speakers. Most of the existing works on boundary detection use either knowledge-based features or representations learnt from a convolutional neural network (CNN) based architecture, considering word segments. However, we hypothesize that combining knowledge-based features and learned representations could improve the boundary detection task’s performance. For this, we consider a fusion-based model considering deep neural network (DNN) and CNN, where CNNs are used for learning representations and DNN is used to combine knowledge-based features and learned representations. Further, unlike existing data-driven methods, we consider two CNNs for learning representation, one for word segments and another for word-final syllable segments. Experiments on Boston University radio news and Switchboard corpora show the benefit of the proposed fusionbased approach compared to a baseline using knowledge-based features only and another baseline using feature representations from CNN only. Index Terms—Boundary detection, human-computer interaction, computer-assisted pronunciation tutoring, CNN based representation learning

Abstract

Recently, there is an increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking advantage of low amounts of labelled corpora in multiple languages. With multilingualism becoming common in today’s world, there has been increasing interest in code-switching ASR as well. In code-switching, multiple languages are freely interchanged within a single sentence or between sentences. The success of low-resource multilingual and code-switching (MUCS) ASR often depends on the variety of languages in terms of their acoustics, linguistic characteristics as well as the amount of data available and how these are carefully considered in building the ASR system. In this MUCS 2021 challenge, we would like to focus on building MUCS ASR systems through two different subtasks related to a total of seven Indian languages, namely Hindi, Marathi, Odia, Tamil, Telugu, Gujarati and Bengali. For this purpose, we provide a total of ∼600 hours of transcribed speech data, comprising train and test sets, in these languages, including two code-switched language pairs, Hindi-English and Bengali-English. We also provide baseline recipes1 for both the subtasks with 30.73% and 32.45% word error rate on the MUCS test sets, respectively. Index Terms: Multilingual, Code-switching, low-resource

Abstract

In this study, listeners of varied Indian nativities are asked to listen and recognize TIMIT utterances spoken by American speakers. We have three kinds of responses from each listener while they recognize an utterance: 1. Sentence difficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of the utterance. From these transcriptions, word error rate (WER) is calculated and used as a metric to evaluate the similarity between the recognized and the original sentences. The sentences selected in this study are categorized into three groups: Easy, Medium and Hard, based on the frequency of occurrence of the words in them. We observe that the sentence, speaker difficulty ratings and the WERs increase from easy to hard categories of sentences. We also compare the human speech recognition performance with that using three automatic speech recognition (ASR) under following three combinations of acoustic model (AM) and language model (LM): ASR1) AM trained with recordings from speakers of Indian origin and LM built on TIMIT text, ASR2) AM using recordings from native American speakers and LM built on text from LIBRI speech corpus, and ASR3) AM using recordings from native American speakers and LM build on LIBRI speech and TIMIT text. We observe that HSR performance is similar to that of ASR1 whereas ASR3 achieves the best performance. Speaker nativity wise analysis shows that utterances from speakers of some nativity are more difficult to recognize by Indian listeners compared to few other nativities. Index Terms: human speech recognition, automatic speech recognition

Abstract

Research and fabrication of micro devices hold the key to low power devices, sensors and lab-on-a-chip systems which are in much need today for diagnosis, IOT etc. Cost of setting up a fabrication laboratory and conducting photolithography is high due to need of expensive equipment. We present a design of low-cost UV exposure system which can be used for both high resolution and low resolution photolithography. In first part, we present the study of irradiance distribution patterns of different UV LED arrangements. We use uniformity as a criterion for selecting the arrangement that gives the best illumination uniformity in largest area. The maximum, minimum and average exposure energy and uniformity have been presented. Using these results, we present an optimal system design for UV photolithography. The novelty of our work is simulating the UV exposure system for different parameters for making an optimal choice for a versatile system (high energy & low energy), different uniformity for different resolution to save power and have better control.

Abstract

A TeV scale leptoquark (LQ) is one of the promising explanations of the recent anomalies in the semileptonic decays of 퐵 mesons. Among the various LQs, the vector U1 is capable of explaining the anomalies in both 푅퐷(∗) and 푅퐾 (∗) observables. We use the current LHC data to put bounds on the parameter space of U1 relevant for the anomalies. Precise bounds are drawn by recasting the latest 휏휏 and 휇휇 searches by the ATLAS and CMS collaborations. We find that it is imperative to include the resonant production modes for obtaining limits in the low mass regions. For higher mass points, the non-resonant production modes play a dominant role.

Abstract

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Abstract

One of the major challenges in the health care sector is that approximately 80% of generated data remains unstructured and unused. Since it is difficult to handle unstructured data from electronic medical record systems, it tends to be neglected for analyses in most hospitals and medical centers. Therefore, there is a need to analyze unstructured big data in health care systems so that we can optimally utilize and unearth all unexploited information from it. Objective: In this study, we aimed to extract a list of diseases and associated keywords along with the corresponding time durations from an indigenously developed electronic medical record system and describe the possibility of analytics from the acquired datasets. Methods: We propose a novel, finite-state machine to sequentially detect and cluster disease names from patients’ medical history. We defined 3 states in the finite-state machine and transition matrix, which depend on the identified keyword. In addition, we also defined a state-change action matrix, which is essentially an action associated with each transition. The dataset used in this study was obtained from an indigenously developed electronic medical record system called eyeSmart that was implemented across a large, multitier ophthalmology network in India. The dataset included patients’past medical history and contained records of 10,000 distinct patients. Results: We extracted disease names and associated keywords by using the finite-state machine with an accuracy of 95%, sensitivity of 94.9%, and positive predictive value of 100%. For the extraction of the duration of disease, the machine’s accuracy was 93%, sensitivity was 92.9%, and the positive predictive value was 100%. Conclusions: We demonstrated that the finite-state machine we developed in this study can be used to accurately identify disease names, associated keywords,