# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 4 IberSpeech-RTVE 2022 Submitted Systems
### Text and Speech Alignment Challenge
#### 4.3.1 Alignment of Re-Speaking Generated Subtitles (TaSAC-ST)  
Only one team submitted results to this evaluation:  
* **GTTS**[27]. University of the Basque Country UPV/EHU (Spain) This system was originally developed as a baseline for the TaSAC-BP subtask and was adjusted to meet the conditions (output file format, etc.) of TaSAC-ST. Acoustic models were trained on cross-domain (non-RTVE) materials, with different channels, background/environment conditions, speakers, etc. The original GTTS submission consisted of two systems (primary and contrastive-1), which applied the same approach but different acoustic models (trained on two independent sets of non-RTVE data). The primary system used a 332-h training set consisting of clean read speech in Basque and Spanish extracted from generic acoustic databases (including Mozilla Common Voice), while the contrastive-1 system used a 1000-h training set consisting of clean spontaneous speech in Basque and Spanish extracted from Basque Parliament plenary sessions. Two late (post-key) systems (contrastive-2 and contrastive-3, trained on the same datasets as the primary and contrastive-1 systems, respectively) were also submitted to the evaluation, yielding improved performance thanks to a kernel modification in the dynamic programming algorithm which provided more compact alignment hypotheses. The four GTTS systems relied on a set of acoustic models used to perform an unrestricted phone decoding of the audio signal, without any language or phonological models to help the decoding. Given an audio file \(X\) and the corresponding STM file with the subtitles \(S\), an off-the-shelf end-to-end neural-network-based phone decoder (built with wav2 letter++, see [28]) was applied to \(X\), which produced a recognized sequence of phone-like units \(p_{X}\) (with timing information attached). On the other hand, an in-house rule- and dictionary-based grapheme-to-phoneme (G2P) converter was applied to \(S\), which produced a reference sequence of phone-like units \(p_{S}\) (with word and subtitle information attached). The two sequences of phone-like units were aligned under the criterion of maximizing the number of matches in the alignment path. Finally, the timing information was transferred from \(p_{X}\) to \(p_{S}\) and a new STM file was created, identical to the source STM except for the timestamps, which were obtained from the alignment. 3.2 Alignment and Validation of Speech Signals with Partial and Inaccurate Text Transcriptions (TaSAC-BP)  
For this evaluation, the organizing team developed a baseline system that aimed to establish a reference mark for participants. The baseline system was based on an off-the-shelf phone decoder (built with wav2 letter++, see [28]), an in-house Grapheme-to-Phoneme (G2P) converter, and a dynamic programming alignment algorithm maximizing the number of matches in the alignment path. Given an audio \(X\) and the paired text \(S\), the phone decoder was applied to \(X\) to get a phonetic sequence \(p_{X}\) (with timestamps attached), the G2P converter was applied to \(S\) to get a second phonetic sequence \(p_{S}\) (with word info attached), then the two sequences \(p_{X}\) and \(p_{S}\) were aligned and the timing information was passed from \(p_{X}\) to \(p_{S}\), and from \(p_{S}\) to words. Word timestamps were post-processed to fill small gaps between words and the word score was computed as the phone recognition rate in the alignment. Finally, the acceptance threshold was optimized on the development set and applied to the development and test sets. Only one team submitted results to this evaluation:  
* **BUT**. BUT Speech@FIT research group ([PERSON] and [PERSON], from Brno University of Technology, Brno, Czech Republic) This submission leveraged the output of some of the ASR systems developed by the BCN2 BRNO team for the Speech to Text Challenge [21].