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20212024
most citedILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale

7 citations · 13 across the 15 of their papers we have counts for

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6 papers · 1 filter

eess.AS20243 cited

Two-pass Endpoint Detection for Speech Recognition

Anirudh Raju, Aparna Khare, Di He +9

Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accur…

eess.AS2024

Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech Recognition

David M. Chan, Shalini Ghosh, Hitesh Tulsiani +2

While word error rates of automatic speech recognition (ASR) systems have consistently fallen, natural language understanding (NLU) applications built on top of ASR systems still a…

eess.AS2023

Discriminative Speech Recognition Rescoring with Pre-trained Language Models

Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu +3

Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained…

eess.AS2023

Personalization for BERT-based Discriminative Speech Recognition Rescoring

Jari Kolehmainen, Yile Gu, Aditya Gourav +4

Recognition of personalized content remains a challenge in end-to-end speech recognition. We explore three novel approaches that use personalized content in a neural rescoring step…

eess.AS2023

Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers

Grant P. Strimel, Yi Xie, Brian King +3

Streaming speech recognition architectures are employed for low-latency, real-time applications. Such architectures are often characterized by their causality. Causal architectures…

eess.AS20222 cited

Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition

Kai Zhen, Hieu Duy Nguyen, Raviteja Chinta +4

We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators. Our method is inspired from Lloyd-Max compression theory with practi…