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In this study, listeners of varied Indian nativities are asked to listen andrecognize TIMIT utterances spoken by American speakers. We have three kinds ofresponses from each listener while they recognize an utterance: 1. Sentencedifficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of theutterance. From these transcriptions, word error rate (WER) is calculated andused as a metric to evaluate the similarity between the recognized and theoriginal sentences.The sentences selected in this study are categorized intothree groups: Easy, Medium and Hard, based on the frequency ofoccurrence of thewords in them. We observe that the sentence, speaker difficulty ratings and theWERs increase from easy to hard categories of sentences. We also compare thehuman speech recognition performance with that using three automatic speechrecognition (ASR) under following three combinations of acoustic model (AM) andlanguage model(LM): ASR1) AM trained with recordings from speakers of Indianorigin and LM built on TIMIT text, ASR2) AM using recordings from nativeAmerican speakers and LM built ontext from LIBRI speech corpus, and ASR3) AMusing recordings from native American speakers and LM build on LIBRI speech andTIMIT text. We observe that HSR performance is similar to that of ASR1 whereasASR3 achieves the best performance. Speaker nativity wise analysis shows thatutterances from speakers of some nativity are more difficult to recognize byIndian listeners compared to few other nativities

A study on native American English speech recognition by Indian listeners with varying word familiarity level