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We present a frontend for improving robustness of automatic speechrecognition (ASR), that jointly implements three modules within a single model:acoustic echo cancellation, speech enhancement, and speech separation. This isachieved by using a contextual enhancement neural network that can optionallymake use of different types of side inputs: (1) a reference signal of theplayback audio, which is necessary for echo cancellation; (2) a noise context,which is useful for speech enhancement; and (3) an embedding vectorrepresenting the voice characteristic of the target speaker of interest, whichis not only critical in speech separation, but also helpful for echocancellation and speech enhancement. We present detailed evaluations to showthat the joint model performs almost as well as the task-specific models, andsignificantly reduces word error rate in noisy conditions even when using alarge-scale state-of-the-art ASR model. Compared to the noisy baseline, thejoint model reduces the word error rate in low signal-to-noise ratio conditionsby at least 71% on our echo cancellation dataset, 10% on our noisy dataset, and26% on our multi-speaker dataset. Compared to task-specific models, the jointmodel performs within 10% on our echo cancellation dataset, 2% on the noisydataset, and 3% on the multi-speaker dataset.

A Conformer-based ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement and Speech Separation