Search Results for author:
Found 15 papers, 6 papers with code
SPIRE-SIES: A Spontaneous Indian English Speech Corpus
Transcripts for 23 hours is generated and validated which can serve as a spontaneous speech ASR benchmark.
Speaking rate attention-based duration prediction for speed control TTS
With the advent of high-quality speech synthesis, there is a lot of interest in controlling various prosodic attributes of speech.
Model Adaptation for ASR in low-resource Indian Languages
This is where a lot of adaptation and fine-tuning techniques can be applied to overcome the low-resource nature of the data by utilising well-resourced similar languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
An unsupervised segmentation of vocal breath sounds
Dynamic programming with the prior information of the number of breath phases($P$) and breath phase duration($d$) is used to find the boundaries.
Vocal Breath Sound Based Gender Classification
In this study, we explore the use of data-driven and knowledge-based features from vocal breath sounds as well as the classifier complexity for gender classification.
Real-Time MRI Video synthesis from time aligned phonemes with sequence-to-sequence networks
Real-Time Magnetic resonance imaging (rtMRI) of the midsagittal plane of the mouth is of interest for speech production research.
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models
In this work, we investigate the effectiveness of pretrained Self-Supervised Learning (SSL) features for learning the mapping for acoustic to articulatory inversion (AAI).
An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production
We also propose two new evaluation metrics for ATB segmentation separately in contour1 and contour2 to explicitly capture two types of errors in these contours.
A study on native American English speech recognition by Indian listeners with varying word familiarity level
We observe that the sentence, speaker difficulty ratings and the WERs increase from easy to hard categories of sentences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-modal Point-of-Care Diagnostics for COVID-19 Based On Acoustics and Symptoms
The research direction of identifying acoustic bio-markers of respiratory diseases has received renewed interest following the onset of COVID-19 pandemic.
Estimating articulatory movements in speech production with transformer networks
Additionally, on the AAI task, we obtain 1. 5%, 3% and 3. 1% relative gain in CC on the same setups compared to the state-of-the-art baseline.
Multilingual and code-switching ASR challenges for low resource Indian languages
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.
DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics
The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning.
Attention and Encoder-Decoder based models for transforming articulatory movements at different speaking rates
As the range of articulatory motions is correlated with speaking rate, we also analyze amplitude of the transformed articulatory movements at different rates compared to their original counterparts, to examine how well the proposed AstNet predicts the extent of articulatory movements in N2F and N2S.
A comparative study of estimating articulatory movements from phoneme sequences and acoustic features
While an attention network is used for estimating articulatory movement in the case of R2, BLSTM network is used for R1 and R3.
