# MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-to-Text Challenge: Extension
However, the application of state-of-the-art ASR technology to video streaming is a highly complex and challenging task due to real-time and low-latency recognition constraints. The MLLP-VRAIN research group has focused its research efforts in the past two years on streaming ASR. This work aims to describe our latest developments in this area, showing how advanced ASR technology can be successfully applied under streaming conditions, by providing high-quality transcriptions and state-of-the-art system latencies on real-life tasks such as the RTVE (Radiotelevision Espanola) database. Current state-of-the-art ASR systems are based on the hybrid approach, combining Hidden Markov Models (HMM) with deep neural networks (DNN) [4] for both acoustic and language models (AM and LM). For acoustic modelling, deep Bidirectional Long-Short Term Memory (BLSTM) networks have shown to be a robust architecture achieving a good performance in an ample range of ASR tasks [5; 6; 7; 8]. As for language modelling, Transformer-based architectures have achieved very promising results [9; 10], though Long-Short Term Memory (LSTM) recurrent neural networks (RNN) are still in broad use [11]. Apart from hybrid systems, end-to-end systems have received great attention in recent years, including a number of proposals for low-latency streaming decoding [12; 13; 14]. However, despite their simplicity and promising prospects, it is still unclear whether or not they will soon surpass state-of-the-art hybrid systems combining independent models trained from vast amounts of data. In this work, we focus on the hybrid approach. One-pass decoding is a recent development within this approach, where a neural LM is used directly during search [15; 16], instead of the conventional rescoring of lattices or \(n\)-best hypotheses in a two-pass decoding approach [17; 18; 19]. In [20], we have recently extended this architecture for real-time one-pass decoding to include BLSTM AMs with a time sliding window. This window is also used for on-the-fly acoustic feature normalization, thus enabling full streaming ASR hybrid recognition with neural AM and LMs. More recently, this architecture has been refined to include streaming-adapted Transformer LMs besides, or even replacing, LSTM-RNN LMs [21]. Our participation in the Albayzin-RTVE 2020 S2T Challenge consisted of the submission of a primary, performance-focused streaming ASR system, plus three contrastive systems: two latency-focused streaming ASR systems, and one conventional off-line ASR system. These systems capitalized on the full streaming ASR hybrid recognition approach described above, and were built using our in-house transLectures-UPV ASR toolkit (TLK) [22]. In contrast, most other participants used the readily-available Kalditoolkit for DNN-HMM ASR systems [23; 24; 25; 26] with occasional end-to-end systems as contrastive submissions. The rest of the paper is structured as follows. First, Section 2 briefly describes the Albayzin-RTVE 2020 S2T Challenge and the RTVE databases provided by the organizers. Next, Section 3 provides a detailed description of our participant ASR systems. Section 4 presents a report of equivalent ASR systems under the 2018 closed data conditions. Finally, Section 5 gives a summary of the work plus some concluding remarks.