Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative dis- order of the central nervous system identified by motor and non- motor activities abnormalities. PD affects respiration, laryngeal, articulation, resonance, and prosodic aspects of speech production. Detection of PD from speech is a non-invasive approach useful for automatic screening. Perceptual attributes of speech due to PD are manifested as temporal variations in speech. In this regard, current work investigated the use of LSTM and BiLSTM networks with shifted delta cepstral (SDC) features to detect PD from speech. Further in BiLSTM networks, a multi-head attention mechanism is introduced, assuming that each head captures distinct information to detect PD. SDC features obtained from MFCCs, and SFFCCs are used for developing the PD detection system. The performance of the experiments is validated using the PC-GITA database. The experimental results revealed that BiLSTM networks give a rela- tive improvement of 4-5% over the LSTM networks. The use of a multi-head attention mechanism further improved the detection accuracy of the PD detection system, showing that it can capture various discriminative features.

Abstract

Modelling Indian languages is difficult as the number of word forms they present is high. In such cases, prior work has proposed us- ing subwords instead of words as the basic units to model the language. This provides the potential to cover more word forms. Additionally, previous work revealed that all Indian languages have several phonetic similarities. Consequently, multilingual acoustic models have been proposed to counter the data scarcity issue most Indian language datasets have. However, these models use monolin- gual language models (LM). They do not exploit the common basic tokens that certain Indian languages have. This motivated us to im- plement a bilingual subword-based Automatic Speech Recognition (ASR) system for Hindi and Marathi. Further, we try a combination of word-based monolingual LM with bilingual acoustic model to examine the reason for degradation in word-based multilingual ASRs. Our experiments show that multilingual subword-based ASR models outperform their word-based counterparts by upto 9.77% and 26.35% relative Character Error Rate (CER) in the case of Hindi and Marathi respectively.

Abstract

Epoch location is an important parameter for analysis of excitation source information from the speech signals. From several years lot of research have been done to find accurate locations of epochs. Epochs are instants at which vocal tract system are excited signifi- cantly. The prominent location of epochs can be found during the production of speech signals. However, due to the time varying nature of excitation source and the vocal tract system it is difficult to find accurate location of epochs. From various epoch extraction methods, Zero Frequency Filtering (ZFF) is the simplest and most widely used method to find accurate locations of epochs due to its highest identification rate and lowest false alarm rate among other algorithms. ZFF uses Infinite Impulse Response (IIR) filter followed by trend removal blocks however, the filter used in ZFF method is unstable which makes it unsuitable for practical implementation. In the literature many stable implementations of ZFF algorithms have been proposed. Compared to other stable algorithms of ZFF, Zero-phase Zero Frequency Resonator has simple design and gives the highest identification rate among other epoch extraction algo- rithms including ZFF. In this paper, the implementation of ZFF and ZP-ZFR has been proposed on FPGA board using Verilog which is Hardware Description Language (HDL).

Abstract

In this paper, we investigate the use of shifted delta cepstral (SDC) coefficients for detecting disfluencies in two types of speech - stuttered speech and spontaneous lecture-mode speech. SDC features capture temporal variations in the speech signal effectively across several frames. The UCLASS stuttered speech dataset and IIITH-IED dataset are used here to develop frame-level automatic disfluency detection systems for four types of disfluencies and the effect of SDC features on the detection of each disfluency type is examined using MFCC and SFFCC cepstral representations. Overall, it is found that using MFCC+SDC features gives an absolute improvement of 2.98% and 6.02% for stuttered and spontaneous speech disfluency detection respectively over the static MFCC features, while SFFCC+SDC features give an absolute improvement of 4.62% for stutter disfluencies and 7.28% for spontaneous speech disfluencies over the static SFFCC features, showing the importance of considering temporal variations for disfluency detection.

Abstract

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

Abstract

Abstract. We consider Lie algebroids over algebraic spaces (in short we call it as a-spaces) by considering the sheaf of Lie-Rinehart algebras. We discuss about properties of universal enveloping algebroid U (OX, L) of a Lie algebroid L over an a-space (X, OX). This is done by sheafification of the presheaf of universal enveloping algebras for Lie-Rinehart algebras. We review the extent to which structure of the universal enveloping algebroid of Lie algebroids (over special a-spaces) resembles a sheaf of bialgebras. In the sequel we present a version of Poincar´e-Birkhoff-Witt theorem and Cartier-Milnor-Moore theorem for the Lie algebroid.

Abstract

Prior work on constructing challenging tabular inference data centered primarily on human annotation or automatic synthetic generation. Both techniques have their own set of issues. Human annotation, despite its diversity and superior reasoning, struggles from scaling concerns. Synthetic data, on the other hand, despite its scalability, suffers from lack of linguistic and reasoning diversity. In this paper, we address both of these concerns by presenting a recasting approach that semi-automatically generates tabular NLI instances. We transform the table2text dataset ToTTo (Parikh et al., 2020) into a tabular NLI dataset using our proposed framework. We demonstrate the use of our recasted data as an evaluation benchmark as well as augmentation data to improve performance on TabFact (Chen et al., 2020b). Furthermore, we test the effectiveness of models trained on our data on the TabFact benchmark in the zeroshot scenario.

Abstract

This paper describes our submission to the WMT2022 shared metrics task. Our unsupervised metric estimates the translation quality at chunk-level and sentence-level. Source and target sentence chunks are retrieved by using a multi-lingual chunker. Chunk-level similarity is computed by leveraging BERT contextual word embeddings and sentence similarity scores are calculated by leveraging sentence embeddings of Language-Agnostic BERT models. The final quality estimation score is obtained by mean pooling the chunk-level and sentence-level similarity scores. This paper outlines our experiments and also reports the correlation with human judgements for en-de, en-ru and zh-en language pairs of WMT17, WMT18 and WMT19 testsets. Our submission will be made available at https://github. com/AnanyaCoder/WMT22Submission_REUSE

Abstract

In this paper, we describe our submission to the WMT22 metrics shared task. Our metric focuses on computing contextual and syntactic equivalences along with lexical, morphological and semantic similarity. The intent is to capture fluency and context of the MT outputs along with their adequacy. Fluency is captured using syntactic similarity and context is captured using sentence similarity leveraging sentence embeddings. The final sentence translation score is the weighted combination of three similarity scores: a) Syntactic Similarity b) Lexical, Morphological and Semantic Similarity and c) Contextual Similarity. This paper outlines two improved versions of MEE ie, MEE2 and MEE4. Additionally, we perform our experiments on language pairs of en-de, en-ru and zh-en from WMT17-19 testset and further report the correlation with human assessments. Our submission will be made available at https://github. com/AnanyaCoder/WMT22Submission.

Abstract

With climate change becoming a cause of concern worldwide, it becomes essential to gauge people’s reactions. This can help educate and spread awareness about it and help leaders improve decision-making. This work explores the fine-grained classification and Stance detection of climate change-related social media text. Firstly, we create two datasets, ClimateStance and ClimateEng, consisting of 3777 tweets each, posted during the 2019 United Nations Framework Convention on Climate Change and comprehensively outline the dataset collection, annotation methodology, and dataset composition. Secondly, we propose the task of Climate Change stance detection based on our proposed ClimateStance dataset. Thirdly, we propose a fine-grained classification based on the ClimateEng dataset, classifying social media text into five categories: Disaster, Ocean/Water, Agriculture/Forestry, Politics, and General. We benchmark both the datasets for climate change stance detection and fine-grained classification using state-of-the-art methods in text classification. We also create a Reddit-based dataset for both the tasks, ClimateReddit, consisting of 6262 pseudo-labeled comments along with 329 manually annotated comments for the label. We then perform semi-supervised experiments for both the tasks and benchmark their results using the best-performing model for the supervised experiments. Lastly, we provide insights into the ClimateStance and ClimateReddit using part-of-speech tagging and named-entity recognition.