10 accepted papers
Voice Conversion research in recent times has increasingly focused on improving the zero-shot capabilities of existing methods. Despite remarkable advancements, current architectures still tend to struggle in zero-shot cross-lingual settings. They are also often unable to generalize for speakers of
Continued pre-training (CP) offers multiple advantages, like target domain adaptation and the potential to exploit the continuous stream of unlabeled data available online. However, continued pre-training on out-of-domain distributions often leads to catastrophic forgetting of previously acquired kn…
Continued self-supervised (SSL) pre-training for adapting existing SSL models to the target domain has shown to be extremely effective for low-resource Automatic Speech Recognition (ASR). This paper proposes Stable Distillation, a simple and novel approach for SSL-based continued pre-training that b…
In this paper, we propose a new Self-Supervised Learning (SSL) algorithm called data2vec-aqc, for speech representation learning from unlabeled speech data. Our goal is to improve SSL for speech in domains where both unlabeled and labeled data are limited. Building on the recently introduced data2ve…
We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST) [1]. Given an input audio spectrogram, we first patchify and project it into an initial temporal resolution and e…
We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on unlabeled audio data that reduces the need for large amounts of labeled data for audio and speech classification. Our primary aim is to learn au-dio representations that can generalize across a large vari-ety of speech…
In this paper, we investigate the use of pre-trained HuBERT model to build downstream Automatic Speech Recognition (ASR) models using data that have differences in domain, accent and even language. We use the standard ESPnet recipe with HuBERT as pre-trained models whose output is fed as input featu…
In many Indian languages, written characters are organized on sound phonetic principles, and the ordering of characters is the same across many of them. However, while training conventional end-to-end (E2E) Multilingual speech recognition systems, we treat characters or target subword units from dif…
The recent success of the Transformer based sequence-to-sequence framework for various Natural Language Processing tasks has motivated its application to Automatic Speech Recognition. In this work, we explore the application of Transformers on low resource Indian languages in a multilingual framewor…
Code-switching (CS) refers to (inter/intra-word) switching between multiple languages in a single conversation. In multilingual countries like India, CS occurs very often in everyday speech, resulting in a new breed of languages in urban regions like Hinglish (Hindi-English), Tanglish (Tamil-English…