# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Search on Speech Challenge  
The Search on Speech challenge involves searching in audio content a list of terms or queries and it is suitable for groups working on speech indexing/retrieval and speech recognition. In other words, this challenge focuses on retrieving the audio files that contain any of those terms/queries along with the corresponding timestamps.  
This challenge consists of two different tasks:* Spoken Term Detection (STD), where the input to the system is a list of terms, but these terms are unknown when processing the audio. This task must generate a set of occurrences for each term detected in the audio files, along with their timestamps and score as output. This is the same task as in NIST STD 2006 evaluation [17] and Open Keyword Search in 2013 [4], 2014 [5], 2015 [6], and 2016 [7].
* Query-by-Example Spoken Term Detection (QbE STD), where the input to the system is an acoustic query and hence a prior knowledge of the correct word/phone transcription corresponding to each query cannot be used. This task must generate a set of occurrences for each query detected in the audio files, along with their timestamps and score as output, as in the STD task. This QbE STD is the same task as those proposed in MediaEval 2011, 2012, and 2013 [18].  
For the QbE STD task, participants are allowed to make use of the target language information (Spanish) when building their system/s (i.e., system/s can be language-dependent). Nevertheless, participants are strongly encouraged to build language-independent QbE STD systems, as in past MediaEval Search on Speech evaluations, where no information about the target language was given to participants.  
This evaluation defined two different sets of terms/queries for STD and QbE STD tasks: an in-vocabulary (INV) set of terms/queries and an out-of-vocabulary (OOV) set of terms/queries from lexicon and language model perspectives. The OOV set of terms/queries will be composed of out-of-vocabulary words for the LVCSR system. This means that, in case participants employ an LVCSR system for processing the audio for any task (STD, QbE STD), these OOV terms (i.e., all the words that compose the term) must be previously removed from the system dictionary/language model and hence, other methods (e.g., phone-based systems) have to be used for searching OOV terms/queries. Participants can consider OOV words for acoustic model training if they find it suitable.  
Regarding the QbE STD task, three different acoustic examples per query were provided for both development and test datasets. One example was extracted from the same dataset as the one to be searched (hence in-domain acoustic examples). This scenario considered the case in which the user finds a term of interest within a certain speech dataset and he/she wants to search for new occurrences of the same query. The two other examples were recorded by the evaluation organizers and comprised a scenario where the user pronounces the query to be searched (hence out-of-domain acoustic examples). These two out-of-domain acoustic examples amount to 3 s of speech with PCM, 16 kHz, single channel and 16 bits per sample with the microphone of an HP ProBook Core i5, 7 th Gen and with a Sennheiser SC630 USR CTRL microphone with noise cancellation, respectively.  
The queries employed for the QbE STD task were chosen from the STD queries. It should be noted that for both the STD and QbE STD tasks, a multi-word query was considered OOV in case any of the words that form the query was OOV.