Papers by Florian Schiel
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings (L18-1)
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| Challenge: | a new algorithm that pre-segments long speech recordings into manageable chunks is proposed . the run time of classical text-to-speech alignment algorithms is quadratically growing with the length of the input . |
| Approach: | They propose two algorithms that pre-segment long speech recordings into manageable chunks . first algorithm is fast but cannot guarantee short chunks on noisy recordings or erroneous transcriptions a second algorithm delivers short chunk but is less effective in terms of run time and chunk boundary accuracy . |
| Outcome: | The proposed algorithms reduce the run time of the speech segmentation system to under real-time even on recordings that could not previously be processed. |
MOCCA: Measure of Confidence for Corpus Analysis - Automatic Reliability Check of Transcript and Automatic Segmentation (L18-1)
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| Challenge: | The production of speech corpora typically involves manual labor to verify and correct the output of automatic transcription/segmentation processes. |
| Approach: | They propose to use Support Vector Machine/Support Vector Regression and Random Forest to predict transcription errors in an annotated speech corpus. |
| Outcome: | The proposed methods can be implemented as free-to-use common language and resources and technology infrastucture web services. |
A Romanization System and WebMAUS Aligner for Arabic Varieties (2022.lrec-1)
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Jalal Al-Tamimi, Florian Schiel, Ghada Khattab, Navdeep Sokhey, Djegdjiga Amazouz, Abdulrahman Dallak, Hajar Moussa
| Challenge: | The WebMAUS 1 is a suite of webservices that is free for academic users that processes 42 languages and language varieties. |
| Approach: | They propose to develop an Arabic variety-independent romanization system that aims to homogenize and simplify the romanization of the Arabic script. |
| Outcome: | The proposed system is based on the existing Arabic variety-independent WebMAUS services. |
Evaluation of Automatic Formant Trackers (L18-1)
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| Challenge: | Formant trackers are widely used by speech scientists and speech engineers. |
| Approach: | They propose to use four open source formant trackers to evaluate the quality of speech recognition algorithms on the same American English data set. |
| Outcome: | The proposed formant trackers outperform LPC-based and Deep Learning on the American English data set VTR-TIMIT. |