Buchumschlag

What Do Self-Supervised Speech Models Know About Words?

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Pasad, Ankita, Chien, Chung-Ming, Settle, Shane, Livescu, Karen
Format:	Preprint
Veröffentlicht:	2023
Schlagworte:	Computation and Language Machine Learning Audio and Speech Processing
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Self-Supervised Speech Representations are More Phonetic than Semantic
von: Choi, Kwanghee, et al.
Veröffentlicht: (2024)

What do Speech Foundation Models Learn? Analysis and Applications
von: Pasad, Ankita
Veröffentlicht: (2025)

AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement
von: Chou, Ju-Chieh, et al.
Veröffentlicht: (2023)

On the Evaluation of Speech Foundation Models for Spoken Language Understanding
von: Arora, Siddhant, et al.
Veröffentlicht: (2024)

MoshiRAG: Asynchronous Knowledge Retrieval for Full-Duplex Speech Language Models
von: Chien, Chung-Ming, et al.
Veröffentlicht: (2026)

CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition
von: Bartelds, Martijn, et al.
Veröffentlicht: (2025)

Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
von: Fang, Hung-Chieh, et al.
Veröffentlicht: (2024)

DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding
von: Shon, Suwon, et al.
Veröffentlicht: (2024)

What Do Speech Foundation Models Not Learn About Speech?
von: Waheed, Abdul, et al.
Veröffentlicht: (2024)

Towards Early Prediction of Self-Supervised Speech Model Performance
von: Whetten, Ryan, et al.
Veröffentlicht: (2025)

Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech Transformers
von: Lin, Tzu-Quan, et al.
Veröffentlicht: (2022)

Property Neurons in Self-Supervised Speech Transformers
von: Lin, Tzu-Quan, et al.
Veröffentlicht: (2024)

Enhancing the Stability of LLM-based Speech Generation Systems through Self-Supervised Representations
von: Martín-Cortinas, Álvaro, et al.
Veröffentlicht: (2024)

Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget
von: Liu, Andy T., et al.
Veröffentlicht: (2024)

Self-Supervised Speech Models Encode Phonetic Context via Position-dependent Orthogonal Subspaces
von: Choi, Kwanghee, et al.
Veröffentlicht: (2026)

Training and Inference Efficiency of Encoder-Decoder Speech Models
von: Żelasko, Piotr, et al.
Veröffentlicht: (2025)

Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment
von: Choi, Kwanghee, et al.
Veröffentlicht: (2025)

Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech Recognition
von: Chan, David M., et al.
Veröffentlicht: (2024)

SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
von: Zampierin, Luca, et al.
Veröffentlicht: (2024)

Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing
von: Trinh, Viet Anh, et al.
Veröffentlicht: (2024)

CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing
von: Lu, Yen-Ju, et al.
Veröffentlicht: (2024)

In-Sync: Adaptation of Speech Aware Large Language Models for ASR with Word Level Timestamp Predictions
von: Fan, Xulin, et al.
Veröffentlicht: (2026)

Universal Robust Speech Adaptation for Cross-Domain Speech Recognition and Enhancement
von: Wang, Chien-Chun, et al.
Veröffentlicht: (2026)

Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context
von: Caubrière, Antoine, et al.
Veröffentlicht: (2024)

On the Effects of Heterogeneous Data Sources on Speech-to-Text Foundation Models
von: Tian, Jinchuan, et al.
Veröffentlicht: (2024)

Language Bias in Self-Supervised Learning For Automatic Speech Recognition
von: Storey, Edward, et al.
Veröffentlicht: (2025)

Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing
von: Sarkar, Eklavya, et al.
Veröffentlicht: (2025)

TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
von: Chang, Kai-Wei, et al.
Veröffentlicht: (2026)

Data-Centric Lessons To Improve Speech-Language Pretraining
von: Udandarao, Vishaal, et al.
Veröffentlicht: (2025)

DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models
von: Chang, Heng-Jui, et al.
Veröffentlicht: (2024)

RosettaSpeech: Zero-Shot Speech-to-Speech Translation without Parallel Speech
von: Zheng, Zhisheng, et al.
Veröffentlicht: (2025)

Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses
von: Kim, Sungnyun, et al.
Veröffentlicht: (2025)

What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis
von: Ashihara, Takanori, et al.
Veröffentlicht: (2024)

Gender Bias in Instruction-Guided Speech Synthesis Models
von: Kuan, Chun-Yi, et al.
Veröffentlicht: (2025)

EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning
von: Seth, Ashish, et al.
Veröffentlicht: (2024)

[b]=[d]-[t]+[p]: Self-supervised Speech Models Discover Phonological Vector Arithmetic
von: Choi, Kwanghee, et al.
Veröffentlicht: (2026)

Improving Speech Decoding from ECoG with Self-Supervised Pretraining
von: Yuan, Brian A., et al.
Veröffentlicht: (2024)

Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text
von: Park, Chanho, et al.
Veröffentlicht: (2023)

Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems
von: Park, Taejin, et al.
Veröffentlicht: (2024)

A Contrastive Learning Approach to Mitigate Bias in Speech Models
von: Koudounas, Alkis, et al.
Veröffentlicht: (2024)