6 papers
Segmental Attention Decoding With Long Form Acoustic Encodings
Pawel Swietojanski, Xinwei Li, Mingbin Xu +3
We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to en…
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Zhihong Lei, Xingyu Na, Mingbin Xu +5
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a…
Enhancing CTC-based speech recognition with diverse modeling units
Shiyi Han, Zhihong Lei, Mingbin Xu +2
In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transf…
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices
Mingbin Xu, Alex Jin, Sicheng Wang +8
With increasingly more powerful compute capabilities and resources in today's devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the c…
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization
Zhihong Lei, Ernest Pusateri, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as c…
Acoustic Model Fusion for End-to-end Speech Recognition
Zhihong Lei, Mingbin Xu, Shiyi Han +8
Recent advances in deep learning and automatic speech recognition (ASR) have enabled the end-to-end (E2E) ASR system and boosted the accuracy to a new level. The E2E systems implic…