Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Susanj, Nathan"'
Autor:
Shakiah, Suhaila M., Swaminathan, Rupak Vignesh, Nguyen, Hieu Duy, Chinta, Raviteja, Afzal, Tariq, Susanj, Nathan, Mouchtaris, Athanasios, Strimel, Grant P., Rastrow, Ariya
Publikováno v:
IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar, 2023, pp. 100-107
Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network accelerator (NNA) chips, which leverage highly parallelized fixed-point
Externí odkaz:
http://arxiv.org/abs/2305.07778
Autor:
Zhen, Kai, Radfar, Martin, Nguyen, Hieu Duy, Strimel, Grant P., Susanj, Nathan, Mouchtaris, Athanasios
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency. Among existing QAT methods, one major drawback is that the quantization ce
Externí odkaz:
http://arxiv.org/abs/2210.09188
Autor:
Radfar, Martin, Barnwal, Rohit, Swaminathan, Rupak Vignesh, Chang, Feng-Ju, Strimel, Grant P., Susanj, Nathan, Mouchtaris, Athanasios
The recurrent neural network transducer (RNN-T) is a prominent streaming end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly consists of stacks of LSTMs. Very recently, as an alternative to LSTM layers, the Conformer architecture
Externí odkaz:
http://arxiv.org/abs/2209.14868
Autor:
Zhen, Kai, Nguyen, Hieu Duy, Chinta, Raviteja, Susanj, Nathan, Mouchtaris, Athanasios, Afzal, Tariq, Rastrow, Ariya
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 practical adaptations for a feasible computational overhead during training.
Externí odkaz:
http://arxiv.org/abs/2206.15408
Autor:
Wei, Kai, Knox, Dillon, Radfar, Martin, Tran, Thanh, Muller, Markus, Strimel, Grant P., Susanj, Nathan, Mouchtaris, Athanasios, Omologo, Maurizio
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems. Prosodic features such as energy and pitch have been shown to be useful for DAC. Despite their importance, little research has explored neural
Externí odkaz:
http://arxiv.org/abs/2205.05590
Autor:
Wei, Kai, Tran, Thanh, Chang, Feng-Ju, Sathyendra, Kanthashree Mysore, Muniyappa, Thejaswi, Liu, Jing, Raju, Anirudh, McGowan, Ross, Susanj, Nathan, Rastrow, Ariya, Strimel, Grant P.
Publikováno v:
ASRU2021
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio. While dialogue history has been exploited to improve conventional text-based natu
Externí odkaz:
http://arxiv.org/abs/2112.06743