9 citations · 12 across the 4 of their papers we have counts for
4 papers
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Hu Hu, Xuesong Yang, Zeynab Raeesy +6
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DA…
Knowledge Distillation and Data Selection for Semi-Supervised Learning in CTC Acoustic Models
Prakhar Swarup, Debmalya Chakrabarty, Ashtosh Sapru +3
Semi-supervised learning (SSL) is an active area of research which aims to utilize unlabelled data in order to improve the accuracy of speech recognition systems. The current study…
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Surabhi Punjabi, Harish Arsikere, Zeynab Raeesy +11
Multilingual ASR technology simplifies model training and deployment, but its accuracy is known to depend on the availability of language information at runtime. Since language ide…
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition
Surabhi Punjabi, Harish Arsikere, Sri Garimella
Building conversational speech recognition systems for new languages is constrained by the availability of utterances that capture user-device interactions. Data collection is both…