Example-based automatic phonetic transcription

Phonetic transcriptions are an important resource in different research areas such as speech recognition or linguistics. Establishing phonetic transcriptions by hand is an exhausting process therefore it seems reasonable to develop an application that automatically creates phonetic transcriptions for given audio data. Current state-of-the-art systems for automatic phonetic transcription (APT) are mostly phone recognizers based on Hidden Markov models (HMMs). We present a different approach for APT especially designed for transcription with a large inventory of phonetic symbols. In contrast to most systems which are model-based, our approach is non-parametric using techniques derived from concatenative speech synthesis and template-based speech recognition. This example-based approach not only produces draft transcriptions that just need to be corrected instead of created from scratch but also provides a validation mechanism for ensuring consistency within the corpus.

Implementations of this transcription framework are available as standalone Java software and extension to the ELAN linguistic annotation software. The transcription system was tested with audio files and reference transcriptions from the Austrian Pronunciation Database (ADABA) and compared to an HMM-based system trained on the same data set. The example-based and the HMM-based system achieve comparable phone recognition rates. A combination of rule-based and example-based APT in a constrained phone recognition scenario returned the best results.





  • Leitner, C., Schickbichler, M., Petrik, S. (2010). Example-Based Automatic Phonetic Transcription. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC), Valletta, Malta. [pdf]
  • Leitner, C. (2008). Data-Based Automatic Phonetic Transcription. Diploma Thesis. Graz University of Technology, Graz, Austria. [pdf]


SPSC Cross Reference
Research Area: