Abstract

The scarcity of high-quality annotated data presents a sig- nificant challenge in developing effective end-to-end speech- to-text translation (ST) systems, particularly for low-resource languages. This paper explores the hypothesis that weakly la- beled data can be used to build ST models for low-resource lan- guage pairs. We constructed speech-to-text translation datasets with the help of bitext mining using state-of-the-art sentence encoders. We mined the multilingual Shrutilipi corpus to build Shrutilipi-anuvaad, a dataset comprising ST data for lan- guage pairs Bengali-Hindi, Malayalam-Hindi, Odia-Hindi, and Telugu-Hindi. We created multiple versions of training data with varying degrees of quality and quantity to investigate the effect of quality versus quantity of weakly labeled data on ST model performance. Results demonstrate that ST systems can be built using weakly labeled data, with performance compa- rable to massive multi-modal multilingual baselines such as SONAR and SeamlessM4T.

Abstract

Computer-Assisted Language Learning systems provide speech exaggerating at mispronounced word locations as a feedback to the L2 learners. Traditionally, expert speakers recordings (which limit the scalability) are considered for this task though there are advancements in text-to-speech (TTS) that can generate native like natural sounding speech. To address these, this work proposes two novel controllable strategies for scalable speech exaggeration. One strategy is direct speech exaggeration that incorporates the proposed label conditioned tokenization in GlowTTS. Another strategy is cascading the state-of-the-art TTS to a WORLD vocoder with proposed energy and duration modifications. A subset of Tatoeba corpus, that we annotated with prominent words, is used for experimentation. Automatic and manual assessment reveals that the exaggerated speech quality from both direct and cascaded strategy with duration modification is closer to the prominent words in the native speaker's speech.

Abstract

Despite advancements in Speech-to-Speech Translation, maintaining expressiveness between the source and target speech, particularly between English and Indian languages, remains challenging. This study investigates prosodic similarities and variations between English and Malayalam, a language spoken in southern India. A set of 22 prompts from the IViE corpus, covering five categories—simple sentences, WH-questions, questions without morphosyntactic markers, inversion questions, and coordinations—was selected. These prompts, originally spoken by UK speakers, were translated into Malayalam and both language prompts were recorded by bilingual Malayalam speakers while preserving expressiveness. Word-level prominence was manually annotated, and comparisons were made across Indian English, Malayalam, and UK English. The analysis reveals that prominence is retained at key points in Indian English, whereas in Malayalam, it is low due to question specific diacritics and agglutinative nature.

Abstract

Abstract— Transgenic crops have become widespread but have associated health concerns. Testing for transgenic corn is slow and lengthy. In this paper, we used UV-Vis spectroscopy in combination with chemometrics and the PCA-SVM classifier to identify different types of corn samples from each other, including transgenic varieties. The presented methodology and the classifier can differentiate very well between the three different varieties of corn considered in this paper. Although there is a slight improvement in prediction accuracies compared to the state-of-the-art methods, our method is considerably more cost-effective and less time-consuming.

Abstract

Automatic detection of prominence at the word and syllable-levels is critical for building computer-assisted language learning systems. It has been shown that prosody embeddings learned by the current state-of-the-art (SOTA) text-to-speech (TTS) systems could generate word- and syllable-level prominence in the synthesized speech as natural as in native speech. To understand the effectiveness of prosody embeddings from TTS for prominence detection under nonnative context, a comparative analysis is conducted on the embeddings extracted from native and non-native speech considering the prominence-related embeddings: duration, energy, and pitch from a SOTA TTS named FastSpeech2. These embeddings are extracted under two conditions considering: 1) only text, 2) both speech and text. For the first condition, the embeddings are extracted directly from the TTS inference mode, whereas for the second condition, we propose to extract from the TTS under training mode. Experiments are conducted on native speech corpus: Tatoeba, and non-native speech corpus: ISLE. For experimentation, word-level prominence locations are manually annotated for both corpora. The highest relative improvement on word & syllable-level prominence detection accuracies with the TTS embeddings are found to be 13.7% & 5.9% and 16.2% & 6.9% compared to those with the heuristic-based features and self-supervised Wav2Vec-2.0 representations, respectively.

Abstract

—Speech disfluencies in spontaneous communication can be categorized as either typical or atypical. Typical disfluencies, such as hesitations and repetitions, are natural occurrences in everyday speech, while atypical disfluencies are indicative of pathological disorders like stuttering. Distinguishing between these categories is crucial for improving voice assistants (VAs) for Persons Who Stutter (PWS), who often face premature cutoffs due to misidentification of speech termination. Accurate classification also aids in detecting stuttering early in children, preventing misdiagnosis as language development disfluency. This research introduces the IIITH-TISA dataset, the first Indian English stammer corpus, capturing atypical disfluencies. Additionally, we extend the IIITH-IED dataset with detailed annotations for typical disfluencies. We propose Perceptually Enhanced Zero-Time Windowed Cepstral Coefficients (PE-ZTWCC) combined with Shifted Delta Cepstra (SDC) as input features to a shallow Time Delay Neural Network (TDNN) classifier, capturing both local and wider temporal contexts. Our method achieves an average F1 score of 85.01% for disfluency classification, outperforming traditional features.

Abstract

Stuttering is recognized as a prevalent speech disorder that significantly affects individuals worldwide. Identifying and diagnosing in the early stages enhances the quality of life for individuals experiencing atypical speech patterns. Traditional methods for classifying stuttering primarily depend on subjective assessments and short-term acoustic analysis. Although helpful, these methods face limitations in accurately capturing all stutter types due to their inherent subjectivity and temporal constraints. This study uses the long-term average spectrum (LTAS) values for stutter classification derived from various filter banks such as Constant Q, Gamma-tone, and Single-frequency filter banks. It also compares these LTAS-based methods with cepstral coefficients, such as MFCC and ZTWCC. Classifiers such as SVM, LSTM, and Bi-LSTM deep networks were used to study the effectiveness of these representations in accurately discerning stuttered speech from fluent speech and reported the results.

Abstract

Automatic detection and severity assessment of dysarthria are crucial for delivering targeted therapeutic interventions to patients. While most existing research focuses primarily on speech modality, this study introduces a novel approach that leverages both speech and text modalities. By employing cross-attention mechanism, our method learns the acoustic and linguistic similarities between speech and text representations. This approach assesses specifically the pronunciation deviations across different severity levels, thereby enhancing the accuracy of dysarthric detection and severity assessment. All the experiments have been performed using UA-Speech dysarthric database. Improved accuracies of 99.53% and 93.20% in detection, and 98.12% and 51.97% for severity assessment have been achieved when speaker-dependent and speaker-independent, unseen and seen words settings are used. These findings suggest that by integrating text information, which provides a reference linguistic knowledge, a more robust framework has been developed for dysarthric detection and assessment, thereby potentially leading to more effective diagnoses.

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.