Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
This work addresses the challenge of ASR for low-resourced languages by enabling training without aligned data, though it appears incremental as it builds on existing unsupervised techniques.
The paper tackles the problem of automatic speech recognition for low-resourced languages lacking aligned audio-text data by proposing an unsupervised framework using unaligned speech and text, achieving results on a read English dataset without specifying concrete performance numbers.
Automatic speech recognition (ASR) has been widely researched with supervised approaches, while many low-resourced languages lack audio-text aligned data, and supervised methods cannot be applied on them. In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned. In the first stage, each word-level audio segment in the utterances is represented by a vector representation extracted by a sequence-of-sequence autoencoder, in which phonetic information and speaker information are disentangled. Secondly, semantic embeddings of audio segments are trained from the vector representations using a skip-gram model. Last but not the least, an unsupervised method is utilized to transform semantic embeddings of audio segments to text embedding space, and finally the transformed embeddings are mapped to words. With the above framework, we are towards unsupervised ASR trained by unaligned text and speech only.