241 citations · 1.2k across the 60 of their papers we have counts for
66 papers
Linguistically Informed Evaluation of Multilingual ASR for African Languages
Fei-Yueh Chen, Lateef Adeleke, C. M. Downey
Word Error Rate (WER) mischaracterizes ASR models' performance for African languages by combining phonological, tone, and other linguistic errors into a single lexical error. By co…
DM-Codec: Distilling Multimodal Representations for Speech Tokenization
Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin +6
Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional…
Toward Joint Language Modeling for Speech Units and Text
Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu +5
Speech and text are two major forms of human language. The research community has been focusing on mapping speech to text or vice versa for many years. However, in the field of lan…
Generative Pre-training for Speech with Flow Matching
Alexander H. Liu, Matt Le, Apoorv Vyas +3
Generative models have gained more and more attention in recent years for their remarkable success in tasks that required estimating and sampling data distribution to generate high…
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS
Po-chun Hsu, Ali Elkahky, Wei-Ning Hsu +6
Self-supervised learning (SSL) techniques have achieved remarkable results in various speech processing tasks. Nonetheless, a significant challenge remains in reducing the reliance…
EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis
Tu Anh Nguyen, Wei-Ning Hsu, Antony D'Avirro +10
Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fash…
Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale
Matthew Le, Apoorv Vyas, Bowen Shi +8
Large-scale generative models such as GPT and DALL-E have revolutionized the research community. These models not only generate high fidelity outputs, but are also generalists whic…
Scaling Speech Technology to 1,000+ Languages
Vineel Pratap, Andros Tjandra, Bowen Shi +13
Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to ab…
DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning
Alexander H. Liu, Heng-Jui Chang, Michael Auli +2
In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distil…
Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech
Maryam Fazel-Zarandi, Wei-Ning Hsu
Self-supervised learning leverages unlabeled data effectively, improving label efficiency and generalization to domains without labeled data. While recent work has studied generali…
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation
Mohamed Anwar, Bowen Shi, Vedanuj Goswami +3
We introduce MuAViC, a multilingual audio-visual corpus for robust speech recognition and robust speech-to-text translation providing 1200 hours of audio-visual speech in 9 languag…
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations
Jiachen Lian, Alexei Baevski, Wei-Ning Hsu +1
Self-supervision has shown great potential for audio-visual speech recognition by vastly reducing the amount of labeled data required to build good systems. However, existing metho…
Scaling Laws for Generative Mixed-Modal Language Models
Armen Aghajanyan, Lili Yu, Alexis Conneau +7
Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens fr…
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement
Wei-Ning Hsu, Tal Remez, Bowen Shi +2
Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present ta…
Efficient Speech Representation Learning with Low-Bit Quantization
Ching-Feng Yeh, Wei-Ning Hsu, Paden Tomasello +1
With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity. In effort to improve the efficiency…
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…
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
Alexei Baevski, Arun Babu, Wei-Ning Hsu +1
Current self-supervised learning algorithms are often modality-specific and require large amounts of computational resources. To address these issues, we increase the training effi…
Speech-to-Speech Translation For A Real-world Unwritten Language
Peng-Jen Chen, Kevin Tran, Yilin Yang +13
We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard te…
Simple and Effective Unsupervised Speech Translation
Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen +5
The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled d…
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Paden Tomasello, Akshat Shrivastava, Daniel Lazar +12
End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging a…
u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality
Wei-Ning Hsu, Bowen Shi
While audio-visual speech models can yield superior performance and robustness compared to audio-only models, their development and adoption are hindered by the lack of labeled and…
Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT
Bowen Shi, Abdelrahman Mohamed, Wei-Ning Hsu
This paper investigates self-supervised pre-training for audio-visual speaker representation learning where a visual stream showing the speaker's mouth area is used alongside speec…
On-demand compute reduction with stochastic wav2vec 2.0
Apoorv Vyas, Wei-Ning Hsu, Michael Auli +1
Squeeze and Efficient Wav2vec (SEW) is a recently proposed architecture that squeezes the input to the transformer encoder for compute efficient pre-training and inference with wav…
Simple and Effective Unsupervised Speech Synthesis
Alexander H. Liu, Cheng-I Jeff Lai, Wei-Ning Hsu +3
We introduce the first unsupervised speech synthesis system based on a simple, yet effective recipe. The framework leverages recent work in unsupervised speech recognition as well…