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Example-Based Automatic Phonetic Transcription

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.