Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR

Zhehuai Chen
Yu Zhang
Pedro Moreno Mengibar
Nanxin Chen
IEEE SLT(2022)


Training state-of-the-art Automated Speech Recognition (ASR) models typically requires a substantial amount of transcribed speech. In this work, we demonstrate that a modality-matched joint speech and text model introduced in~\cite{zhehuai2021} can be leveraged to train a massively multilingual ASR model without any transcribed speech. In most zero resource conditions, lack of transcribed speech also implies lack of lexicons. This paper explores the use of jointly learnt speech and text representations in a massively multilingual, zero transcribed speech, real-world setting to expand the set of languages covered by ASR models with only unlabeled speech and text in the target languages. We define the task to cover $102$ languages, where transcribed speech is available in $52$ of these languages and can be used to improve end-to-end ASR quality on the remaining $50$. First, we show that by combining speech representations with byte-level text representations coupled with the effective use of language embeddings, we can dramatically reduce the resource requirements for deploying an ASR model to a new language. On the FLEURS dataset, this approach is able to reduce the CER on languages with no transcribed speech from 64.1\% to 29.6\%, a relative reduction of 54\%. Second, using a subset of Indic languages we show that the proposed method can learn effectively from languages with transcribed speech even when there is limited to no graphemeic overlap with the target languages, reducing the average CER of the target languages from 56.3 to 17.2. We believe this is the first demonstration that competitive ASR performance can be achieved for an unseen language using no language resources other than text and untranscribed speech.

Research Areas