Multilingual speech recognition systems often use an input language code in order to prompt the transcription in the target language. However, the spoken language in the input audio may not always match the language code, as often prevalent in multilingual societies. This language mismatch can significantly reduce ASR quality. We present a technique to identify and mitigate this issue. We combine off-the-shelf language-ID and language verification models to determine the language code input to the ASR model. The language verification model acts as a gate that decides when to trust the provided language code or use the output of the language-ID model. We compare these approaches with baselines that include vanilla language-ID based and language-independent ASR models. Our experiments on YouTube, SPRING-INX and FLEURS datasets shows the efficacy of the proposed model especially in the mismatched language code setting.

The state-of-the-art conformer used in automatic speech recognition combines feed-forward, convolution and multi-headed self-attention layers in a single model that is trained end-to-end with a decoder network. While this end-to-end training is simple and beneficial for word error rate, it restricts the ability to perform inference with the model at different operating points of word error rate and latency. Existing approaches to overcome this limitation include cascaded encoders and variable attention context models. We propose an alternative approach, called Modular Conformer training, which splits the Conformer model into a backbone convolutional model and attention submodels, which are added at each layer. We conduct experiments with a few training techniques on the Librispeech and Librilight corpus. We show that dropping-out the attention layers during the training of the backbone model allows for the largest WER improvements upon adding fine-tuned attention submodels, without impacting the WER of the backbone model itself.

The development of end-to-end models has largely sped up the research in massively multilingual automatic speech recognition (MMASR). Previous research has demonstrated the feasibility to build high quality MMASR models. In this work, we study the impact of adding more languages and propose a lifelong learning approach to build high quality MMASR systems. Experiments on a 66-language Voice Search task show that we can take a model built on 15 languages and continue training to obtain a 32-language model and similarly to further build a 67-language model. More importantly, models developed in this way achieve better quality compared to those trained from scratch. It maintains similar performance on old languages and achieves competitive results on new ones. This would potentially speed up the development of universal ASR models that recognize speech from any language, any domain and any environment by reusing knowledge learned beforehand.

Automatic speech recognition (ASR) systems need to be accurate, have low latency, and effectively handle language switching in order to be useful for the 60% of the world population that speaks more than one language. Thus, we propose a truly multilingual first-pass and monolingual second-pass streaming on-device ASR system based on the recently developed Cascaded Encoders model. The streaming first-pass recognizes multilingual speech without needing language information, providing real-time transcription, even for code-switching speech. The second-pass uses a language dependent right context encoder to improve the recognition accuracy. On a 9 language Voice Search task, we find that a system combining shared causal encoder with decoders and non-causal encoders replicated per-language reduces word error rate (WER) by 4.4% relative to monolingual baselines. We further show this design to be parameter efficient, outperforming other architectures when matched in the number of parameters.

When trained on related or low-resource languages, multilingual speech recognition models often outperform their monolingual counterparts. However, these models can suffer from loss in performance for high resource or unrelated languages. We investigate the use of a mixture-of-experts approach to assign per-language parameters in the model to increase network capacity in a structured fashion. We introduce a novel variant of this approach, ‘informed experts’, which attempts to tackle inter-task conflicts by eliminating gradients from other tasks in these task-specific parameters. We conduct experiments on a real-world task with English, French and four dialects of Arabic to show the effectiveness of our approach. Our model matches or outperforms the monolingual models for almost all languages, with gains of as much as 31% relative. Our model also outperforms the baseline multilingual model for all languages by up to 9% relative.