Word identification.

<p>Word identification based on the reconstructed spectrograms was assessed using a set of 47 individual words and pseudowords from a single speaker in a high density 4 mm grid experiment. The speech recognition algorithm is described in the text. (A) Distribution of identification rank for all 47 words in the set. Median identification rank is 0.89 (black arrow), which is higher than 0.50 chance level (dashed line; <i>p</i><0.0001; randomization test). Statistical significance was assessed by a randomization test in which a null distribution of the median was constructed by randomly shuffling the word pairs 10,000 times, computing median identification rank for each shuffle, and calculating the percentile rank of the true median in the null distribution. Best performance was achieved after smoothing the spectrograms with a 2-D box filter (500 ms, 2 octaves). (B) Receiver operating characteristic (ROC) plot of identification performance (red curve). Diagonal black line indicates no predictive power. (C) Examples of accurately (right) and inaccurately (left) identified words. Left: reconstruction of pseudoword “heef” is poor and leads to a low identification rank (0.13). Right: reconstruction of pseudoword “thack” is accurate and best matches the correct word out of 46 other candidate words (identification rank = 1.0). (D) Actual and reconstructed word similarity is correlated (<i>r</i> = 0.41). Pair-wise similarity between the original spectrograms of individual words is correlated with pair-wise similarity between the reconstructed and original spectrograms. Plotted values are computed prior to spectrogram smoothing used in the identification algorithm. Gray points denote the similarity between identical words.