Sentence Recognition Materials Based on Frequency of Word Use and Lexical Confusability
The sentence stimuli developed in this project combined aspects from several traditional approaches to speech audiometry. Sentences varied with respect to frequency of word use and phonetic confusability. Familiar consonant-vowel-consonant words, nouns and modifiers, were used to form 500 sentences of seven to nine syllables. Based on concepts from the Neighborhood Activation Model for spoken word recognition, each sentence contained three key words that were all characterized as high- or low-use frequency and high or low lexical confusability. Use frequency was determined by published indices of word use, and lexical confusability was defined by a metric based on the number of other words that were similar to a given word using a single phoneme substitution algorithm. Thirty-two subjects with normal hearing were randomly assigned to one of seven presentation levels in quiet, and an additional 32 listeners were randomly assigned to a fixed-level noise background at one of six signal-to-noise ratios. The results indicated that in both quiet and noise listening conditions, high-use words were more intelligible than low-use words, and there was an advantage for phonetically unique words; the position of the key word in the sentence was also a significant factor. These data formed the basis for a sequence of experiments that isolated significant nonacoustic sources of variation in spoken word recognition.
Abbreviations: CVC = consonant-vowel-consonant, HD = high frequency of use word from a dense neighborhood, HS = high frequency of use word from a sparse neighborhood, LD = low frequency of use word from a dense neighborhood, LS = low frequency of use word from a sparse neighborhood, NAM = Neighborhood Activation Model, SIN = Speech in Noise, SNR = signal-to-noise ratio
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07. März 2022
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