5 papers
In-Context Adaptation of Encoder-Decoder Models in Speech Recognition
Yen Meng, Sharon Goldwater, Hao Tang
In-context learning offers an appealing approach to adapt automatic speech recognition (ASR) models to new speakers, accents, and domains by providing speech-text pairs as demonstr…
Omnilingual SONAR: Cross-Lingual and Cross-Modal Sentence Embeddings Bridging Massively Multilingual Text and Speech
Omnilingual SONAR Team, João Maria Janeiro, Pere-Lluís Huguet Cabot +17
Cross-lingual sentence encoders typically cover only a few hundred languages and often trade downstream quality for stronger alignment, limiting their adoption. We introduce OmniSO…
Omnilingual ASR: Open-Source Multilingual Speech Recognition for 1600+ Languages
Omnilingual ASR team, Gil Keren, Artyom Kozhevnikov +30
Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages be…
Whisper Has an Internal Word Aligner
Sung-Lin Yeh, Yen Meng, Hao Tang
There is an increasing interest in obtaining accurate word-level timestamps from strong automatic speech recognizers, in particular Whisper. Existing approaches either require addi…
Effective Context in Neural Speech Models
Yen Meng, Sharon Goldwater, Hao Tang
Modern neural speech models benefit from having longer context, and many approaches have been proposed to increase the maximum context a model can use. However, few have attempted…