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Contextualized end-to-end speech recognition with contextual phrase prediction network
Contextual information plays a crucial role in speech recognition technologies and
incorporating it into the end-to-end speech recognition models has drawn immense interest …
incorporating it into the end-to-end speech recognition models has drawn immense interest …
Can contextual biasing remain effective with Whisper and GPT-2?
End-to-end automatic speech recognition (ASR) and large language models, such as
Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite …
Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite …
Towards contextual spelling correction for customization of end-to-end speech recognition systems
Contextual biasing is an important and challenging task for end-to-end automatic speech
recognition (ASR) systems, which aims to achieve better recognition performance by biasing …
recognition (ASR) systems, which aims to achieve better recognition performance by biasing …
Improving contextual recognition of rare words with an alternate spelling prediction model
Contextual ASR, which takes a list of bias terms as input along with audio, has drawn recent
interest as ASR use becomes more widespread. We are releasing contextual biasing lists to …
interest as ASR use becomes more widespread. We are releasing contextual biasing lists to …
Slot-triggered contextual biasing for personalized speech recognition using neural transducers
End-to-end (E2E) automatic speech recognition (ASR) models have been found to perform
well on general transcription tasks but often fail to correctly recognize words that occur …
well on general transcription tasks but often fail to correctly recognize words that occur …
Adaptive contextual biasing for transducer based streaming speech recognition
By incorporating additional contextual information, deep biasing methods have emerged as
a promising solution for speech recognition of personalized words. However, for real-world …
a promising solution for speech recognition of personalized words. However, for real-world …
PromptASR for contextualized ASR with controllable style
Prompts are crucial to large language models as they provide context information such as
topic or logical relationships. Inspired by this, we propose PromptASR, a framework that …
topic or logical relationships. Inspired by this, we propose PromptASR, a framework that …
Minimising biasing word errors for contextual ASR with the tree-constrained pointer generator
Contextual knowledge is essential for reducing speech recognition errors on high-valued
long-tail words. This paper proposes a novel tree-constrained pointer generator (TCPGen) …
long-tail words. This paper proposes a novel tree-constrained pointer generator (TCPGen) …
[HTML][HTML] Knowledge-aware audio-grounded generative slot filling for limited annotated data
Manually annotating fine-grained slot-value labels for task-oriented dialogue (ToD) systems
is an expensive and time-consuming endeavour. This motivates research into slot-filling …
is an expensive and time-consuming endeavour. This motivates research into slot-filling …
Contextualized end-to-end automatic speech recognition with intermediate biasing loss
Contextualized end-to-end automatic speech recognition has been an active research area,
with recent efforts focusing on the implicit learning of contextual phrases based on the final …
with recent efforts focusing on the implicit learning of contextual phrases based on the final …