Abstract

n personalizing interactions with smart devices, identifying keywords in an open-vocabulary context is crucial. Previous methods for open-vocabulary keyword spotting relied on a shared embedding space created by audio and text encoders. However, they suffered from heterogeneous modality representations, causing audio-text mismatch. To tackle this issue, our proposed framework utilizes knowledge from a pre-trained text-to-speech (TTS) system. This knowledge transfer incorporates awareness of audio projections into text representations derived from the text encoder. Consequently, this approach aids in preventing false triggers in scenarios such as closely related pronunciations of audio-text pairs. Additionally, our proposed approach benefits from the keyword embedding calculation only during keyword enrollment phase. The proposed system gives consistent performance across all the word lengths.

Abstract

Many real-world speech processing applications depend on the accurate estimation of epoch location. In a real-world scenario, speech may contain multiple speakers or multiple emotions in a single utterance. In the literature, the performance of epoch extraction algorithm was evaluated on speech utterance consisting of a single speaker or single emotion alone. In this study, speech utterance from different speakers, and emotions are combined to simulate multi-speaker and multi-emotion scenario, respectively. Five state-of-the-art epoch extraction methods were used to evaluate the performance for multi-speaker, multi-emotion, and voice disorder scenarios. We also analysed the performance of state-of-the-art epoch extraction methods after applying the region-wise approach on existing methods. CMU arctic, and Berlin emotional database is used in this study to simulate the multi-speaker, and multi-emotion scenarios, respectively for analysing the performance of epoch extraction algorithms. Saarbruecken voice disorder database is used to perform the experiments for voice disorder scenario. The result of this study indicates that performance of state-of-the art epoch extraction methods (which depend on pitch period) degrades in terms of false alarm rate (FAR) for these three scenarios. By applying the region-wise approach on these methods for extraction of epoch locations, FAR is reduced by 5% to 8% for the real-world scenario. It was also observed from this study, that the performance of dynamic programming based methods remains unchanged even after applying the region-wise approach on existing methods.

Abstract

The demand for high-quality parallel speech data has been increasing as deep learning-based Speech to Speech Machine Translation (SSMT) and automatic dubbing approaches gain popularity in speech applications. Traditional and well-established speech applications such as Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) heavily rely on large corpus of monolingual speech and the corresponding text. While there is a wealth of parallel text data available for both English and Indic languages, parallel speech data is available only for English and other European languages, yet it often lacks natural prosody and semantic alignment between the languages. For achieving cross-lingual prosody transfer, end-to-end SSMT models, and high-quality dubbing from English to Hindi, in this work, an English-Hindi parallel bilingual speech-text corpus named lIlT-Speech Twins 1.0 is created. This data contains twin-like English and Hindi speech-text pairs obtained from publicly available children's stories in both the languages, through manual and automatic processing. Starting with 8 stories in each language, totaling around 4 hours of audio, the final outcome was a 2-hour dataset. This was achieved through systematic segmentation, re-moval of non-speech background audio, and sentence-by-sentence alignment to ensure accurate meaning in both languages. In addition to ensuring proper alignment and transcription, this dataset offers a rich source of natural prosody, expressions, and emotions, due to the narrative diversity within the stories. The dataset also provides sig-nificant speaker variability, with different characters being voiced by various speakers, enhancing the richness of the lIlT-Speech Twins 1.0 corpus.

Abstract

Stuttering constitutes a pervasive speech disorder with a global impact on individuals. Detecting stuttering in speech would aid speech pathologists in monitoring fluency over time and enhance the quality of life for those with atypical speech patterns. Presently, the available public datasets tailored for stuttering identification are confined to the utterance level, with labels ascribed to individual utterances. However, in real-time situations, a considerable proportion of individuals grappling with stuttering manifest dysfluency at the syllable level. This limitation markedly constrains the effectiveness and applicability of dysfluency detection systems. In an effort to address this lacuna in knowledge, this study introduces a novel dataset comprising audio recordings directly obtained from native Kannada-speaking individuals afflicted with the stuttering disorder at the syllable level and also provides detailed information on the dataset's creation, including patient selection criteria and clip labelling. Additionally, benchmark models trained on this distinctive dataset are presented as part of this research endeavor.

Abstract

Identifying user-defined keywords is crucial for personalizing interactions with smart devices. Previous approaches of user-defined keyword spotting (UDKWS) have relied on short-term spectral features such as mel frequency cepstral coefficients (MFCC) to detect the spoken keyword. However, these features may face challenges in accurately identifying closely related pronunciation of audio-text pairs, due to their limited capability in capturing the temporal dynamics of the speech signal. To address this challenge, we propose to use shifted delta coefficients (SDC) which help in capturing pronunciation variability (transition between connecting phonemes) by incorporating long-term temporal information. The performance of the SDC feature is compared with various baseline features across four different datasets using a cross-attention based end-to-end system. Additionally, various configurations of SDC are explored to find the suitable temporal context for the UDKWS task. The experimental results reveal that the SDC feature outperforms the MFCC baseline feature, exhibiting an improvement of 8.32% in area under the curve (AUC) and 8.69% in terms of equal error rate (EER) on the challenging Libriphrase-hard dataset. Moreover, the proposed approach demonstrated superior performance when compared to state-of-the-art UDKWS techniques.

Abstract

Identifying keywords in an open-vocabulary context is crucial for personalizing interactions with smart devices. Previous approaches to open vocabulary keyword spotting dependon a shared embedding space created by audio and text encoders. However, these approaches suffer from heterogeneous modality representations (i.e., audio-text mismatch). To address this issue, our proposed framework leverages knowledge acquired from a pre-trained text-to-speech (TTS) system. This knowledge transfer allows for the incorporation of awareness of audio projections into the text representations derived from the text encoder. The performance of the proposed approach is compared with various baseline methods across four different datasets. The robustness of our proposed model is evaluated by assessing its performance across different word lengths and in an Out-of-Vocabulary (OOV) scenario. Additionally, the effectiveness of transfer learning from the TTS system is investigated by analyzing its different intermediate representations. The experimental results indicate that, in the challenging LibriPhrase Hard dataset, the proposed approach outperformed the cross-modality correspondence detector (CMCD) method by a significant improvement of 8.22% in area under the curve (AUC) and 12.56% in equal error rate (EER).

Abstract

India's education sector faces a significant challenge due to its linguistic diversity, hindering inclusivity. The dominance of English on the internet underscores the critical need for translating educational content into Indian languages to enhance accessibility. Although Speech-to-Speech Machine Translation (SSMT) technologies exist, their deficiency in reproducing intonation results in monotonous translations, diminishing audience engagement and interest in the content. To address this issue, this paper demonstrates an SSMT application with a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech to give a more engaging experience. The SSMT pipeline also has components like a stress classifier that captures the stress in the source speech and allows it to be utilized during speech generation. The application takes in a speech file and gives a translated speech file with stress transferred from the source.

Abstract

The language diversity in India's education sector poses a significant challenge, hindering inclusivity. Despite the democratization of knowledge through online educational content, the dominance of English, as the internet's lingua franca, limits accessibility, emphasizing the crucial need for translation into Indian languages. Despite existing Speech-to-Speech Machine Translation (SSMT) technologies, the lack of intonation in these systems gives monotonous translations, leading to a loss of audience interest and disengagement from the content. To address this, our paper introduces a dataset with stress annotations in Indian English and also a Text-to-Speech (TTS) architecture capable of incorporating stress into synthesized speech. This dataset is used for training a stress detection model, which is then used in the SSMT system for detecting stress in the source speech and transferring it into the target language speech. The TTS architecture is based on FastPitch and can modify the variances based on stressed words given. We present an Indian English-to-Hindi SSMT system that can transfer stress and aim to enhance the overall quality and engagement of educational content.

Abstract

Speech systems are sensitive to accent variations. This is a challenge in India, which has numerous languages but few linguistic studies on pronunciation variation. The growing number of L2 English speakers reinforces the need to study accents and L1-L2 interactions. We investigate Indian English (IE) accents and report our observations on regional and shared features. Specifically, we observe phonemic variations and phonotactics in speakers’ native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing native language features with IE pronunciations obtained from literature studies and phonetically annotated speech. Hence, we validate Indian language influences on IE by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description of generalised and region-specific IE characteristics, which facilitates accent adaptation of existing speech systems.

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.