An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies

Evaluation campaigns provide a common framework with which the progress of speech technologies can be effectively measured. The aim of this paper is to present a detailed overview of the IberSpeech-RTVE 2022 Challenges, which were organized as part of the IberSpeech 2022 conference under the ongoing series of Albayzin evaluation campaigns. In the 2022 edition, four challenges were launched: (1) speech-to-text transcription; (2) speaker diarization and identity assignment; (3) text and speech alignment; and (4) search on speech. Different databases that cover different domains (e.g., broadcast news, conference talks, parliament sessions) were released for those challenges. The submitted systems also cover a wide range of speech processing methods, which include hidden Markov model-based approaches, end-to-end neural network-based methods, hybrid approaches, etc. This paper describes the databases, the tasks and the performance metrics used in the four challenges. It also provides the most relevant features of the submitted systems and briefly presents and discusses the obtained results. Despite employing state-of-the-art technology, the relatively poor performance attained in some of the challenges reveals that there is still room for improvement. This encourages us to carry on with the Albayzin evaluation campaigns in the coming years.This work was partially supported by Radio Televisión Española through the RTVE Chair at the University of Zaragoza, and Red Temática en Tecnologías del Habla (RED2022-134270-T), funded by AEI (Ministerio de Ciencia e Innovación); It was also partially funded by the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie Grant 101007666; in part by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/ PRTR under Grants PDC2021-120846C41 PID2021-126061OB-C44, and in part by the Government of Aragon (Grant Group T3623R); it was also partially funded by the Spanish Ministry of Science and Innovation (OPEN-SPEECH project, PID2019-106424RB-I00) and by the Basque Government under the general support program to research groups (IT-1704-22), and by projects RTI2018-098091-B-I00 and PID2021-125943OB-I00 (Spanish Ministry of Science and Innovation and ERDF) as well

Sautrela: a highly modular open source speech recognition framework

This paper describes the Sautrela system (www.sautrela.org), a highly modular and pluggable open source framework for generic purpose signal processing, focused on speech recog-nition. The aim of Sautrela is to unify in a single frame-work almost all the tasks related to pattern recognition such as signal processing, model training and decoding. This framework has been developed using the JavaTM Technol-ogy and thus ensures its portability to a large variety of com-puter platforms. 1

Optimizing PLLR Features for Spoken Language Recognition

Phone Log-Likelihood Ratios (PLLR) have been recently introduced as features for spoken language and speaker recognition systems. This representation has proven to be an effective way of retrieving acoustic-phonotactic information into frame-level vectors, which can be easily plugged into state-of-the-art systems. In a previous work, we began the search of reduced representations of PLLRs, as a mean of reducing computational costs. In this paper, we extend this search, by looking for the optimal compromise between feature vector size and system performance. Results achieved by Principal Component Analysis projection on the PLLR space are extensively analyzed. Also, to evaluate the effect of using larger temporal contexts, a Shifted Delta transfor-mation is applied (and its optimal configuration explored) on highly reduced sets of PCA-projected PLLR features, leading to further performance improvements over the best PCA-projected PLLR set

Systems and Evaluation

and Sebastian Tscho ̈ pel Henk van den Heuvel, Eri