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We propose an unsupervised speaker adaptation method inspired by the neuralTuring machine for end-to-end (E2E) automatic speech recognition (ASR). Theproposed model contains a memory block that holds speaker i-vectors extractedfrom the training data and reads relevant i-vectors from the memory through anattention mechanism. The resulting memory vector (M-vector) is concatenated tothe acoustic features or to the hidden layer activations of an E2E neuralnetwork model. The E2E ASR system is based on the joint connectionist temporalclassification and attention-based encoder-decoder architecture. M-vector andi-vector results are compared for inserting them at different layers of theencoder neural network using the WSJ and TED-LIUM2 ASR benchmarks. We show thatM-vectors, which do not require an auxiliary speaker embedding extractionsystem at test time, achieve similar word error rates (WERs) compared toi-vectors for single speaker utterances and significantly lower WERs forutterances in which there are speaker changes.

Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR