Search Results for author:
Found 7 papers, 4 papers with code
When to Use Efficient Self Attention? Profiling Text, Speech and Image Transformer Variants
We present the first unified study of the efficiency of self-attention-based Transformer variants spanning text, speech and vision.
Textless Low-Resource Speech-to-Speech Translation With Unit Language Models
We train and evaluate our models for English-to-German, German-to-English and Marathi-to-English translation on three different domains (European Parliament, Common Voice, and All India Radio) with single-speaker synthesized speech data.
Continual Learning for On-Device Speech Recognition using Disentangled Conformers
Additionally, current speech recognition models and continual learning algorithms are not optimized to be compute-efficient.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Zero-shot Video Moment Retrieval With Off-the-Shelf Models
For the majority of the machine learning community, the expensive nature of collecting high-quality human-annotated data and the inability to efficiently finetune very large state-of-the-art pretrained models on limited compute are major bottlenecks for building models for new tasks.
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality
Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning.
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.
Reduce and Reconstruct: ASR for Low-Resource Phonetic Languages
This work presents a seemingly simple but effective technique to improve low-resource ASR systems for phonetic languages.
