78 citations · 91 across the 14 of their papers we have counts for
14 papers
CS-YODAS: A Mined Dataset of In-the-Wild Code-Switched Speech
Brian Yan, Qingzheng Wang, Matthew Wiesner +9
We present CS-YODAS, a Creative Commons-licensed dataset of in-the-wild code-switched speech mined from multilingual YouTube data. Code-switching (CS), or the alternation between l…
ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath
We introduce ParaSpeechCLAP, a family of dual-encoder models that map speech and text style captions into a shared embedding space, supporting rich intrinsic (speaker-level) and si…
VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing
Zhisheng Zheng, Puyuan Peng, Anuj Diwan +5
We introduce VoiceCraft-X, an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot Text-to-Speech (TTS) synthesis across 11 languages:…
CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
Brian Yan, Injy Hamed, Shuichiro Shimizu +24
We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4…
Rhapsody: A Dataset for Highlight Detection in Podcasts
Younghan Park, Anuj Diwan, David Harwath +1
Podcasts have become daily companions for half a billion users. Given the enormous amount of podcast content available, highlights provide a valuable signal that helps viewers get…
Scaling Rich Style-Prompted Text-to-Speech Datasets
Anuj Diwan, Zhisheng Zheng, David Harwath +1
We introduce Paralinguistic Speech Captions (ParaSpeechCaps), a large-scale dataset that annotates speech utterances with rich style captions. While rich abstract tags (e.g. guttur…
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
When to Use Efficient Self Attention? Profiling Text, Speech and Image Transformer Variants
Anuj Diwan, Eunsol Choi, David Harwath
We present the first unified study of the efficiency of self-attention-based Transformer variants spanning text, speech and vision. We identify input length thresholds (tipping poi…
Textless Speech-to-Speech Translation With Limited Parallel Data
Anuj Diwan, Anirudh Srinivasan, David Harwath +1
Existing speech-to-speech translation (S2ST) models fall into two camps: they either leverage text as an intermediate step or require hundreds of hours of parallel speech data. Bot…
Continual Learning for On-Device Speech Recognition using Disentangled Conformers
Anuj Diwan, Ching-Feng Yeh, Wei-Ning Hsu +4
Automatic speech recognition research focuses on training and evaluating on static datasets. Yet, as speech models are increasingly deployed on personal devices, such models encoun…
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality
Anuj Diwan, Layne Berry, Eunsol Choi +2
Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning. Yet, they fail miserably on the recently propos…
Zero-shot Video Moment Retrieval With Off-the-Shelf Models
Anuj Diwan, Puyuan Peng, Raymond J. Mooney
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-…
Multilingual and code-switching ASR challenges for low resource Indian languages
Anuj Diwan, Rakesh Vaideeswaran, Sanket Shah +19
Recently, there is increasing interest in multilingual automatic speech recognition (ASR) where a speech recognition system caters to multiple low resource languages by taking adva…
Reduce and Reconstruct: ASR for Low-Resource Phonetic Languages
Anuj Diwan, Preethi Jyothi
This work presents a seemingly simple but effective technique to improve low-resource ASR systems for phonetic languages. By identifying sets of acoustically similar graphemes in t…