5 citations · 8 across the 11 of their papers we have counts for
11 papers
Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
Daigo Takizawa, Tomohiko Nakamura, Samuele Cornell +3
Neural audio codecs (NACs) have become popular for obtaining speech representations as discrete tokens. Beyond compression, discrete tokens can be used to train self-supervised lea…
The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties
William Chen, Chutong Meng, Jiatong Shi +10
Recent improvements in multilingual ASR have not been equally distributed across languages and language varieties. To advance state-of-the-art (SOTA) ASR models, we present the Int…
On The Landscape of Spoken Language Models: A Comprehensive Survey
Siddhant Arora, Kai-Wei Chang, Chung-Ming Chien +7
The field of spoken language processing is undergoing a shift from training custom-built, task-specific models toward using and optimizing spoken language models (SLMs) which act a…
Findings of the IWSLT 2024 Evaluation Campaign
Ibrahim Said Ahmad, Antonios Anastasopoulos, Ondřej Bojar +42
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and off…
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…
Towards Robust Speech Representation Learning for Thousands of Languages
William Chen, Wangyou Zhang, Yifan Peng +7
Self-supervised learning (SSL) has helped extend speech technologies to more languages by reducing the need for labeled data. However, models are still far from supporting the worl…
On the Evaluation of Speech Foundation Models for Spoken Language Understanding
Siddhant Arora, Ankita Pasad, Chung-Ming Chien +9
The Spoken Language Understanding Evaluation (SLUE) suite of benchmark tasks was recently introduced to address the need for open resources and benchmarking of complex spoken langu…
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
Suwon Shon, Kwangyoun Kim, Yi-Te Hsu +3
The integration of pre-trained text-based large language models (LLM) with speech input has enabled instruction-following capabilities for diverse speech tasks. This integration re…
On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models
Jinchuan Tian, Yifan Peng, William Chen +3
The Open Whisper-style Speech Model (OWSM) series was introduced to achieve full transparency in building advanced speech-to-text (S2T) foundation models. To this end, OWSM models…
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
Jiatong Shi, Shih-Heng Wang, William Chen +8
ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats the models as featur…
Self-Supervised Speech Representations are More Phonetic than Semantic
Kwanghee Choi, Ankita Pasad, Tomohiko Nakamura +3
Self-supervised speech models (S3Ms) have become an effective backbone for speech applications. Various analyses suggest that S3Ms encode linguistic properties. In this work, we se…