Dubno J R, Levitt H
J Acoust Soc Am. 1981 Jan;69(1):249-61. doi: 10.1121/1.385345.
Acoustic measurements of nonsense syllables in quiet and in noise were used to predict the pattern of consonant confusions made under those conditions. Eleven sets of nonsense syllables were presented to normal-hearing subjects in quiet and at a +5 dB speech-to-noise ratio, at five speech levels. A set of acoustic characteristics of the speech stimuli were chosen for analysis and measured using digital processing techniques. Results of the recognition task revealed significant effects of consonant voicing, position and vowel context on syllable recognition. The performance-intensity function of the quiet condition rises more steeply than the function obtained in noise. The effect of noise on consonant recognition is dependent upon the manner in which the consonant is produced, and the location of maximum constriction. Differences in the absolute values of the acoustic parameters of syllable pairs were used to predict their percentage of confusion. A set of acoustic variables was isolated which was found to be the best predictor of confusion percentages. Although the sets of acoustic variables were different for various syllable types and test conditions, three variables (consonant energy, consonant spectral peaks, consonant-to-noise ratio) were used in a majority of the predictions.
通过对无意义音节在安静环境和噪声环境下进行声学测量，来预测在这些条件下产生的辅音混淆模式。向听力正常的受试者在安静环境以及信噪比为 +5 dB 的情况下，以五种语音强度呈现了十一组无意义音节。选择了一组语音刺激的声学特征进行分析，并使用数字处理技术进行测量。识别任务的结果显示，辅音的浊音性、位置和元音语境对音节识别有显著影响。安静环境下的性能 - 强度函数比在噪声环境中获得的函数上升得更陡峭。噪声对辅音识别的影响取决于辅音的发音方式以及最大收缩的位置。音节对声学参数绝对值的差异被用于预测它们的混淆百分比。分离出了一组声学变量，发现它们是混淆百分比的最佳预测指标。尽管对于不同的音节类型和测试条件，声学变量集有所不同，但在大多数预测中使用了三个变量（辅音能量、辅音频谱峰值、辅音与噪声的比率）。
