Facebook's speech recognition model supports 51 different languages

Facebook researchers have developed what they claim is the largest automatic speech recognition (ASR) model of its kind -- a model that learned to understand words in 51 languages after training on over 16,000 hours of voice recordings. In a paper published on the preprint server Arxiv.org, the coauthors say the system, which contains around a billion parameters, improves speech recognition performance up to 28.8% on one benchmark compared with baselines. Designing a single model to recognize speech in multiple languages is desirable for several reasons. It simplifies the backend production pipeline, for one thing, and studies have shown training multilingual models on similar languages can decrease overall word error rate (WER). Facebook's model -- a so-called joint sequence-to-sequence (Seq2Seq) model -- was trained while sharing the parameters from an encoder, decoder, and token set across all languages. The encoder maps input audio sequences to intermediate representations while the decoder maps the representations to output text, and the token set simplifies the process of working with many languages by sampling sentences at different frequencies.