9 citations · 22 across the 6 of their papers we have counts for
6 papers
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios
Aswin Shanmugam Subramanian, Amit Das, Naoyuki Kanda +3
We extend the frameworks of Serialized Output Training (SOT) to address practical needs of both streaming and offline automatic speech recognition (ASR) applications. Our approach…
Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey
Fatemeh Jamshidi, Gary Pike, Amit Das +1
In the domain of Music Information Retrieval (MIR), Automatic Music Transcription (AMT) emerges as a central challenge, aiming to convert audio signals into symbolic notations like…
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model
Jinyu Li, Rui Zhao, Eric Sun +4
While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion…
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units
Amit Das, Jinyu Li, Guoli Ye +2
The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issue…
Advancing Acoustic-to-Word CTC Model
Jinyu Li, Guoli Ye, Amit Das +2
The acoustic-to-word model based on the connectionist temporal classification (CTC) criterion was shown as a natural end-to-end (E2E) model directly targeting words as output units…
Advancing Connectionist Temporal Classification With Attention Modeling
Amit Das, Jinyu Li, Rui Zhao +1
In this study, we propose advancing all-neural speech recognition by directly incorporating attention modeling within the Connectionist Temporal Classification (CTC) framework. In…