  In this study, listeners of varied Indian nativities are asked to listen and
recognize TIMIT utterances spoken by American speakers. We have three kinds of
responses from each listener while they recognize an utterance: 1. Sentence
difficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of the
utterance. From these transcriptions, word error rate (WER) is calculated and
used as a metric to evaluate the similarity between the recognized and the
original sentences.The sentences selected in this study are categorized into
three groups: Easy, Medium and Hard, based on the frequency ofoccurrence of the
words in them. We observe that the sentence, speaker difficulty ratings and the
WERs increase from easy to hard categories of sentences. We also compare the
human speech recognition performance with that using three automatic speech
recognition (ASR) under following three combinations of acoustic model (AM) and
language model(LM): ASR1) AM trained with recordings from speakers of Indian
origin and LM built on TIMIT text, ASR2) AM using recordings from native
American speakers and LM built ontext from LIBRI speech corpus, and ASR3) AM
using recordings from native American speakers and LM build on LIBRI speech and
TIMIT text. We observe that HSR performance is similar to that of ASR1 whereas
ASR3 achieves the best performance. Speaker nativity wise analysis shows that
utterances from speakers of some nativity are more difficult to recognize by
Indian listeners compared to few other nativities

Audio and Speech Processing;Computation and Language;Sound