Abstract

Stuttering is a speech disorder that affects speech fluency and rhythm, with millions worldwide experiencing it. Early diagnosis and treatment can significantly improve speech fluency and the quality of life for individuals who stutter. Automatic detection of stuttering events can help diagnose, monitor, and develop effective interventions. Therefore, this paper aims to propose a feature space-based classifier for detecting stuttering events in speech. To achieve this, we have investigated Zero Time Windowing Cepstral Coefficients (ZTWCC) as a feature set for stutter detection using classifiers such as SVM, LSTM, and Bidirectional LSTM. We compared the performance of ZTWCC with the standard handcrafted features, such as MFCC, CQCC, and SFFCC, on the SEP- 28K dataset with and without including phase information. The results in both cases indicate that ZTWCC is giving a higher F-1 score than baseline MFCC features.

Abstract

Analysis of Indian English (IE) pronunciation variabilities is useful in ASR and TTS modelling for the Indian context. Prior works characterised IE variabilities by reporting qualitative phonetic rules relative to Received Pronunciation (RP). However, such characterisations lack quantitative descriptors and data-driven analysis of diverse IE pronunciations, which could be due to the scarcity of phonetically labelled data. Furthermore, the versatility of IE stems from the influence of a large diversity of the speakers’ mother tongues and demographic region differences. To address these issues, we consider the corpus Indic TIMIT and manually obtain 13, 632 phonetic transcriptions in addition to those parts of the corpus. By performing a data-driven analysis on 15, 974 phonetic transcriptions of 80 speakers from diverse regions of India, we present a new set of phonetic rules and validate them against the existing phonetic rules to identify their relevance. Finally, we test the efficacy of Grapheme-to-Phoneme (G2P) conversion developed based on the obtained rules considering Phoneme Error Rate (PER) as the metric for performance.

Abstract

This work proposes the utilization of a self-supervised pre-trained network for developing a Language Identification (LID) system catering to low-resource Indian languages. The framework employed is Wav2vec2.0-XLSR-53, pre-trained on 53k hours of unlabeled speech data. The unsupervised training of the model enables it to learn the acoustic patterns specific to a language. Given that languages share phonetic space, multi-lingual pre-training is instrumental in learning cross-lingual information and building systems that cater to low-resource languages. Further fine-tuning with a limited amount of labeled data significantly boosts the model’s accuracy. The results showcase a relative improvement of 33.2% over the DNN-A (DNN with attention) model and 19.04% over Dense Resnets for the Language Identification task on the IIITH-ILSC database using the proposed features (Shivang Gupta and Kowshik Siva Sai Motepalli share first authorship).

Abstract

Parkinson’s disease (PD) is a neurological disorder that affects the nerves responsible for controlling movements. Automatic detection methods used for PD detection from the speech signal are important as they offer benefits such as reliability, repeatability, and cost-effectiveness. In this paper, cepstral features derived from zero-time windowing (ZTW) based time-frequency analysis method for automatic detection of PD were explored. This study used the PC-GITA database to evaluate the performance using support vector machine classifier and neural networks. The performance of the system is also compared with conventional mel-frequency cepstral coeffcients (MFCCs) features. Cepstral features derived from ZTW method shown better performance than baseline feature. The best performance of the PD detection system obtained using the cepstral features derived from ZTW method in terms of classification accuracy is 73.6% . Index Terms—Parkinson’s Disease, Zero-time windowing, Automatic detection methods using speech signal.

Abstract

In this paper, the intrinsic characteristics of speech modulations are estimated to propose the instant modulation spectral features for efficient emotion recognition. This feature representation is based on single frequency filtering (SFF) technique and higher order nonlinear energy operator. The speech signal is decomposed into frequency sub-bands using SFF, and associated nonlinear energies are estimated with higher order nonlinear energy operator. Then, the feature vector is realized using cepstral analysis. The high-resolution property of SFF technique is exploited to extract the amplitude envelope of the speech signal at a selected frequency with good time-frequency resolution. The fourth order nonlinear energy operator provides noise robustness in estimating the modulation components. The proposed feature set is tested for the emotion recognition task using the i-vector model with the probabilistic linear discriminant scoring scheme, support vector machine and random forest classifiers. The results demonstrate that the performance of this feature representation is better than the widely used spectral and prosody features, achieving detection accuracy of 85.75%, 59.88%, and 65.78% on three emotional databases, EMODB, FAUAIBO, and IEMOCAP, respectively. Further, the proposed features are found to be robust in the presence of additive white Gaussian and vehicular noises.

Abstract

Electroencephalography (EEG) provides the temporal resolution required to map the neural activations for studying motor movements and control. This study aims to compare the power amplitude of electrodes covering the central and frontal regions of a 32-channel scalp EEG. The activations from a standard index finger-tapping and a game paradigm are analyzed. Twenty-five right-handed and five left-handed healthy subjects (range = 18–30 years; mean = 24.25 years; SD = 3.96 years) participated in this study. A novel single-frequency filter (SFF) bank was applied to identify the peak amplitude from the power spectral density plots. The results show that the gaming paradigm yields lower or comparable power amplitudes than the standard finger tapping. We observed that the right-hand finger tapping by the left-handed subjects shows lower between-subject dispersion in amplitude. Nonparametric Spearman correlation showed no association between game scores and power amplitude for the right-handed participants. However, for left-handers, both positive and negative associations were observed. This study demonstrates the efficacy of SFF for extracting power amplitudes with a better signalto-noise ratio, which has implications in BCI and motor rehabilitation applications. The findings support the role of game paradigms for motor movement research and in understanding bilateral hemispheric activations in cognitive tasks.

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

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

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

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.