Automatic speech recognition (ASR) in Sanskrit is interesting, owing to the
various linguistic peculiarities present in the language. The Sanskrit language
is lexically productive, undergoes euphonic assimilation of phones at the word
boundaries and exhibits variations in spelling conventions and in
pronunciations. In this work, we propose the first large scale study of
automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact
of unit selection in Sanskrit ASR. In this work, we release a 78 hour ASR
dataset for Sanskrit, which faithfully captures several of the linguistic
characteristics expressed by the language. We investigate the role of different
acoustic model and language model units in ASR systems for Sanskrit. We also
propose a new modelling unit, inspired by the syllable level unit selection,
that captures character sequences from one vowel in the word to the next vowel.
We also highlight the importance of choosing graphemic representations for
Sanskrit and show the impact of this choice on word error rates (WER). Finally,
we extend these insights from Sanskrit ASR for building ASR systems in two
other Indic languages, Gujarati and Telugu. For both these languages, our
experimental results show that the use of phonetic based graphemic
representations in ASR results in performance improvements as compared to ASR
systems that use native scripts.

本文对梵语 ASR 进行了首次大规模研究，研究了单元选择对梵语 ASR 的影响，并发布了 78 小时的梵语 ASR 数据集，研究不同声学模型和语言模型单元在 ASR 系统中的角色，提出了一个新的模型单元，并强调选择语文文字表示对词错误率的影响。