no code implementations • 6 May 2022 • Sankaran Panchapagesan, Arun Narayanan, Turaj Zakizadeh Shabestary, Shuai Shao, Nathan Howard, Alex Park, James Walker, Alexander Gruenstein
Acoustic Echo Cancellation (AEC) is essential for accurate recognition of queries spoken to a smart speaker that is playing out audio.
no code implementations • 26 Apr 2022 • Arun Narayanan, James Walker, Sankaran Panchapagesan, Nathan Howard, Yuma Koizumi
Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech.
no code implementations • 1 Nov 2021 • Yuma Koizumi, Shigeki Karita, Arun Narayanan, Sankaran Panchapagesan, Michiel Bacchiani
Furthermore, by analyzing the predicted target SNRi, we observed the jointly trained network automatically controls the target SNRi according to noise characteristics.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 1 Aug 2018 • Anirudh Raju, Sankaran Panchapagesan, Xing Liu, Arindam Mandal, Nikko Strom
Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents.
no code implementations • 5 May 2017 • Ming Sun, Anirudh Raju, George Tucker, Sankaran Panchapagesan, Geng-Shen Fu, Arindam Mandal, Spyros Matsoukas, Nikko Strom, Shiv Vitaladevuni
Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67. 6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.