Automatic labeling of speech

To evaluate the performance of a speech recognition system, large databases of labeled speech, including various speakers, noise conditions, and vocabularies, are necessary. This paper describes a method for automatically labeling speech data. In the past, speech has been labeled manually, typically by listening to and viewing waveforms through real-time, interactive computer I/O stations. This process is slow and tedious, and accounts for the shortage of large speech databases. The automatic labeling method reported here uses dynamic programming to align a script which is produced as the output of a recognition system, and a known script. The alignment gives a tentative labeling which can be refined by repeating the training, recognition, and alignment processes. The method was used to label a 50 speaker database of 140,000 digits.