  We propose a new method for the calculation of error rates in Automatic
Speech Recognition (ASR). This new metric is for languages that contain half
characters and where the same character can be written in different forms. We
implement our methodology in Hindi which is one of the main languages from
Indic context and we think this approach is scalable to other similar languages
containing a large character set. We call our metrics Alternate Word Error Rate
(AWER) and Alternate Character Error Rate (ACER).
  We train our ASR models using wav2vec 2.0\cite{baevski2020wav2vec} for Indic
languages. Additionally we use language models to improve our model
performance. Our results show a significant improvement in analyzing the error
rates at word and character level and the interpretability of the ASR system is
improved upto $3$\% in AWER and $7$\% in ACER for Hindi. Our experiments
suggest that in languages which have complex pronunciation, there are multiple
ways of writing words without changing their meaning. In such cases AWER and
ACER will be more useful rather than WER and CER as metrics. Further, we open
source a new benchmarking dataset of 21 hours for Hindi with the new metric
scripts.
