Suitability of syllable-based modeling units for end-to-end speech recognition in Sanskrit and other Indian languages

Author:

Anoop Chandran SavithriORCID,Ramakrishnan Angarai GanesanORCID

Funder

Science and Engineering Research Board

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference54 articles.

1. Automatic speech recognition in sanskrit: A new speech corpus and modelling insights;Adiga;Findings of the Association for Computational Linguistics: ACL-IJCNLP,2021

2. Anoop, C. S., & Ramakrishnan, A. G. (2021a). CTC-Based End-To-End ASR for the Low Resource Sanskrit Language with Spectrogram Augmentation. In Proc. of the national conference on communications (pp. 1–6).

3. Investigation of different G2P schemes for speech recognition in Sanskrit;Anoop,2021

4. Anoop, C. S., & Ramakrishnan, A. G. (2022). Exploring a Unified ASR for Multiple South Indian Languages Leveraging Multilingual Acoustic and Language Models. In Proc. of the IEEE spoken language technology workshop (pp. 830–837).

5. Effectiveness of mining audio and text pairs from public data for improving ASR systems for low-resource languages;Bhogale,2022

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