A recent paradigm shift in artificial intelligence has seen the rise of foundation models, such as the large language models and the universal speech models. With billions of model parameters and trained with a wide range of data, these foundation models are expected to have a better generalization to different downstream tasks. Efficient adaptation is the key to leveraging these foundation models in a new task or domain. In this paper, we compare several popular parameter-efficient tuning methods, such as vector adaptation, residual adapters, low-rank adapter (LoRA) and prompt-tuning, for automatic speech recognition (ASR) domain adaptation. We use the connectionist temporal classification (CTC) model with Conformer encoder and fused it with a universal language model. We study the effect of adapting either or both of the Conformer encoder and the universal language model. We carry out extensive experiments to study these methods under different hyper-parameter settings and the effect of combining some of these methods. We find that combining vector adaptation and residual adapters with increasing bottleneck dimension achieved the best performance.

Contextual Spelling Correction (CSC) models are used to improve automatic speech recognition (ASR) quality given userspecific context. Typically, context is modeled as a large set of text spans to compare against a given ASR hypothesis using some distance measure (text, phonetic, or neural embedding). In this work we propose a CSC system based on a single Large Language Model (LLM) adapted with prompt tuning. Our approach is shown to be data efficient, and does not require dedicated serving. Our system exhibits advanced contextualization capabilities, such as support for phonetic spellings, cross-lingual scripts, and context specified as topics, with little to no data engineering. On voice assistant datasets, our system achieves $7.8 \%$ absolute word error rate reduction from a reference ASR system with relevant context and improving upon other contextualization solutions. Finally, we test our system in a prompt-injection attack scenario and report vulnerabilities and mitigations.

We present a joint Speech and Language Model (SLM), a multitask, multilingual, and dual-modal model that takes advantage of pretrained foundational speech and language models. SLM freezes the pretrained foundation models to maximally preserves their capabilities, and only trains a simple adapter with just 1% (156M) of the foundation models’ parameters. This adaptation not only leads SLM to achieve strong performance on conventional tasks such as automatic speech recognition (ASR) and automatic speech translation (AST), but also unlocks the novel capability of zero-shot instruction-following for more diverse tasks. Given a speech input and a text instruction, SLM is able to perform unseen generation tasks including contextual biasing ASR using real-time context, dialog generation, speech continuation, and question answering. Our approach demonstrates that the representational gap between pretrained speech and language models is narrower than one would expect, and can be bridged by a simple adaptation mechanism. As a result, SLM is not only efficient to train, but also inherits strong capabilities already present in foundation models of different modalities.

Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimized in the first pretraining stage, and the standard supervised finetuning resumes in the second stage. In this paper, we propose an end-to-end (E2E) Joint Unsupervised and Supervised Training (JUST) method to combine the supervised RNN-T loss and the self-supervised contrastive and masked language modeling (MLM) losses. We validate its performance on the public dataset Multilingual LibriSpeech (MLS), which includes 8 languages and is extremely imbalanced. On MLS, we explore (1) JUST trained from scratch, and (2) JUST finetuned from a pretrained checkpoint. Experiments show that JUST can consistently outperform other existing state-of-the-art methods, and beat the monolingual baseline by a significant margin, demonstrating JUST’s capability of handling low-resource languages in multilingual ASR. Our average WER of all languages outperforms average monolingual baseline by 33.3%, and the state-of-the-art 2-stage XLSR by 32%. On low-resource languages like Polish, our WER is less than half of the monolingual baseline and even beats the supervised transfer learning method which uses external supervision.

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance model performance. However, these approaches mostly focus on in-domain performance for public datasets. In this study, we utilize the combination of self- and semi-supervised learning methods to solve unseen domain adaptation problems in a large-scale production setting for online ASR model. This approach demonstrates that using the source domain data with a small fraction of the target domain data (3%) can recover the performance gap compared to a full data baseline: 13.5% relative WER improvement for target domain data.