# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Text and Speech Alignment Challenge
#### 3.3.1 Alignment of Re-Speaking Generated Subtitles (TaSAC-ST)  
The IberSPEECH-RTVE 2022 Text and Speech Alignment Challenge aims to evaluate the text-to-speech alignment systems on the actual problem of synchronizing re-speaking subtitles with the corresponding audio. The task assesses the state of the art of offline alignment technology. The purpose is to provide subtitles without delay for a new broadcast. In this task, participants are supplied with the subtitles as they originally appeared on TV, including the start and end timestamps of each subtitle. Participants must provide an output with the exact same sequence of subtitles but with new start and end timestamps for each subtitle. It should be noted that re-speaking subtitles often differ from the actual spoken words. If the speech is too fast the re-speaker tends to suppress words (deletions)or even to paraphrase, which introduces a new level of difficulty in the alignment process. The performance is measured by computing the time differences between the aligned start and end timestamps given by the alignment systems and the reference timestamps derived from a careful manual alignment. Performance Metric  
The average program time-error metric (APTEM) is the primary metric for the Text and Speech alignment task. For each program in the test, a program time-error metric (PTEM) will be calculated and the final score will be computed by averaging the PTEM of each program in the test. \[APTEM=\frac{1}{M}\sum_{m=1}^{M}PTEM(m), \tag{5}\]  
where \(M\) is the number of programs in the test dataset, and the PTEM is computed as follows: Given the probability distribution of the time difference between the reference and aligned start and end timestamps of each subtitle in a program, the PTEM is computed as the median value of the distribution. Let's define the start-time error for the n-th subtitle, \(TE_{s}(n)\), as:  
\[TE_{s}(n)=|T_{\textit{ref}}(n)-T_{\textit{salig}}(n)|, \tag{6}\]  
where \(T_{\textit{ref}}(n)\) and \(T_{\textit{salig}}(n)\) are the start timestamps of the reference and the automatic alignment for the n-th subtitle. Similarly, the end-time error, \(TE_{e}(n)\), is defined as:  
\[TE_{e}(n)=|T_{\textit{ref}}(n)-T_{\textit{salig}}(n)|, \tag{7}\]  
where \(T_{\textit{ref}}(n)\) and \(T_{\textit{salig}}(n)\) are the end timestamps of the reference and the automatic alignment for the n-th subtitle. Then, the time error of the n-th subtitle, \(TE(n)\) is computed as:  
\[TE(n)=TE_{s}(n)+TE_{e}(n). \tag{8}\]  
The PTEM is defined as  
\[PTEM=med([TE(1),TE(2),...,TE(N)]), \tag{9}\]  
where \(med()\) is the median operator and \(N\) is the number of subtitles broadcast in the TV program. 3.2 Alignment and Validation of Speech Signals with Partial and Inaccurate Text Transcriptions (TaSAC-BP)  
Over the last years, with the widespread adoption of data-intensive deep learning approaches to ASR, the semi-supervised collection of training data for ASR has gained renewed interest. The Internet is plenty of resources pairing speech and text. Sometimes the paired text is an accurate transcription of the spoken content, but frequently it is only a loose or partial transcription or even a translation to some other language. Therefore, a text-to-speech alignment system able to detect and extract accurately paired speech and text segments becomes a very valuable tool. The second task of the Text-and-Speech Alignment Challenge (TaSAC-BP) was designed with that goal in mind. The alignment systems would deal with a long audio file, including sections in Spanish and Basque, but the paired texts (which could be partial or approximate transcripts of the audio) would cover only the Spanish sections. The audio parts in Basque were not expected to be paired with any text, though some words or word fragments (proper names, technical terms, etc.) may actually match (and be wrongly paired with) text in Spanish.