Digging into sentence intelligibility: Interactions with noise

Autor: Richard A. Wright, Matthew C. Kelley, Marina Oganyan, Benjamin V. Tucker
Rok vydání: 2022
Předmět:
Zdroj: The Journal of the Acoustical Society of America. 152:A174-A174
ISSN: 0001-4966
Popis: Implicit in the design of many intelligibility and perception studies is the assumption that noise masks all sentence stimuli equivalently, at least when they are controlled for length and structure. For example, controlled and normed sentences, such as the IEEE Harvard set, are typically thought to be equally affected by masker-noise. However, previous research has established that spoken stimuli of different structures, lengths, or complexities, are differentially masked (e.g. nonsense versus real words, words with different usage frequencies), therefore it is possible that masking affects even controlled sentences differentially . Using the UAW speech intelligibility dataset we analyzed Levenshtein Distance values based on transcriptions from over 900 native listeners of English to over 604 sentences from the UWNU IEEE sentence corpus. Each sentence was presented in noise at three SNRs ( + 2, 0, −2 dB). We find that the sentences are not equally intelligible. Moreover, there is an interaction with SNR level and sentence. In other words, the intelligibility of the sentences is impacted differentially by masker-noise. The results of this study suggest that, as researchers using speech stimuli, we should recognize that there are many sentence level factors that may introduce variance or otherwise affect the outcomes of our studies.
Databáze: OpenAIRE