MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-to-Text Challenge: Extension

Abstract

This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the Albayzín-RTVE 2020 Speech-to-Text Challenge, and includes an extension of the work consisting of building and evaluating equivalent systems under the closed data conditions from the 2018 challenge. The primary system (p-streaming_1500ms_nlt) was a hybrid ASR system using streaming one-pass decoding with a context window of 1.5 seconds. This system achieved 16.0% WER on the test-2020 set. We also submitted three contrastive systems. From these, we highlight the system c2-streaming_600ms_t which, following a similar configuration as the primary system with a smaller context window of 0.6 s, scored 16.9% WER points on the same test set, with a measured empirical latency of 0.81 ± 0.09 s (mean ± stdev). That is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative. As an extension, the equivalent closed-condition systems obtained 23.3% WER and 23.5% WER, respectively. When evaluated with an unconstrained language model, we obtained 19.9% WER and 20.4% WER; i.e., not far behind the top-performing systems with only 5% of the full acoustic data and with the extra ability of being streaming-capable. Indeed, all of these streaming systems could be put into production environments for automatic captioning of live media streams.

Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models

“…In addition to these results, there are also included results from our recent publication (Baquero-Arnal et al 2022). In this work there were carried out a comparison against the others submissions in a closed-condition fashion.…”

Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources

“…As it could be expected from the naturalness results previously shown in Table 7.2, there is a strong preference for the LSGAN synthetic utterances over the baseline ones. Baq+22]. It also provides a web interface for post-editing the automatic transcriptions and translations.…”