- Niklas Bühler, A. Waibel, T. Asfour. 2021. Cross-lingual, Language-independent Phoneme Alignment. Abstract: When documenting an unknown language – and especially its pronunciation – for the first time, linguists are oftentimes missing the necessary technology to do so efficiently. The work described in this thesis might facilitate future advancements in this regard by further developing techniques that make the computer-assisted examination of languages more efficient. However, not only the process of manually examining an unseen language is very costly. Even in cases where automatic speech recognition technology is already utilized, the necessary process of data collection and preparation still remains costly. Cross-lingual approaches can alleviate this problem. The goal of this thesis is to apply cross-lingual, multilingual techniques on the task of phoneme alignment, i.e. the task of temporally aligning a phonetic transcript to its corresponding audio recording. Three different neural network architectures are trained on a multilingual data set and utilized as a source of emission probabilities in hybrid HMM/ANN systems. These HMM/ANN systems enable the computation of phoneme alignments via the Viterbi algorithm. By iterating this process, multilingual acoustic models are bootstrapped and the resulting systems are used to cross-lingually align data from a previously unseen target language. Finally, the results are scored and compared against each other.
