TDNNs for phonetic features extraction: a visual exploration

The authors report results obtained in the framework of phonetic feature extraction using TDNNs (time-delay neural networks). Experiments were conducted on a set of 5270 phonemes manually segmented from natural continuous speech uttered by one male speaker. Nearly all scores on binary phonetic features range between 90% and 99%. Comments on the visualization of the weights in several networks are also presented. In particular, it is noted that there exist quantitative similarities between patterns of weights within the TDNN and acoustical characteristics of the phonetic feature