Generative Spoken Language Model based on continuous word-sized audio tokens
This work addresses the need for more efficient and expressive generative models in speech processing, though it is incremental as it adapts text-based word modeling ideas to speech.
The authors tackled the problem of generative spoken language modeling by introducing a model based on word-size continuous audio embeddings, achieving performance on par with discrete unit models while being five times more memory efficient with 200ms units.
In NLP, text language models based on words or subwords are known to outperform their character-based counterparts. Yet, in the speech community, the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter than a phoneme). Taking inspiration from word-based LM, we introduce a Generative Spoken Language Model (GSLM) based on word-size continuous-valued audio embeddings that can generate diverse and expressive language output. This is obtained by replacing lookup table for lexical types with a Lexical Embedding function, the cross entropy loss by a contrastive loss, and multinomial sampling by k-NN sampling. The resulting model is the first generative language model based on word-size continuous embeddings. Its performance is on par with discrete unit GSLMs regarding generation quality as measured by automatic metrics and subjective human judgements. Moreover, it is five times more memory efficient thanks to its large 200ms units. In addition, the embeddings before and after the Lexical Embedder are phonetically and semantically interpretable.