The run time of classical text-to-speech alignment algorithms tends to grow quadratically with the length of the input. This makes it difficult to apply them to very long speech recordings. In this paper, we describe and evaluate two algorithms that pre-segment long recordings into manageable "chunks". The first algorithm is fast but cannot guarantee short chunks on noisy recordings or erroneous transcriptions. The second algorithm reliably delivers short chunks but is less effective in terms of run time and chunk boundary accuracy. We show that both algorithms reduce the run time of the MAUS speech segmentation system to under real-time, even on recordings that could not previously be processed. Evaluation on real-world recordings in three different languages shows that the majority of chunk boundaries obtained with the proposed methods deviate less than 100 ms from a ground truth segmentation. On a separate German studio quality recording, MAUS word segmentation accuracy was slightly improved by both algorithms. The chunking service is freely accessible via a web API in the CLARIN infrastructure, and currently supports 33 languages and dialects.
@InProceedings{POERNER18.105, author = {Nina Poerner and Florian Schiel}, title = "{A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper