7 accepted papers
Self-training in the teacher-student framework generally suffers from the confirmation bias problem, where errors from the teacher are propagated to the student and hence get amplified with multiple iterations. In this paper, we present Monte Carlo Self-training where pseudo labels are generated by…
Semi-supervised training can be performed by jointly optimizing supervised and unsupervised losses. In many settings, supervised and unsupervised losses are inconsistent, and this inconsistency creates instability in training. As a solution, we propose cross-training: instead of training one network…
This paper introduces contrastive siamese (c-siam) network, an architecture for leveraging unlabeled acoustic data in speech recognition. c-siam is the first network that extracts high-level linguistic information from speech by matching outputs of two identical transformer encoders. It contains aug…
In this paper, we present a novel speaker diarization system for streaming on-device applications. In this system, we use a transformer transducer to detect the speaker turns, represent each speaker turn by a speaker embedding, then cluster these embeddings with constraints from the detected speaker…
In this paper we present an end-to-end speech recognition system that can recognize single-channel speech where multiple talkers can speak at the same time (overlapping speech) by using a neural network model based on Recurrent Neural Network Transducer (RNN-T) architecture. We augment the conventio…
In this paper we present an end-to-end speech recognition model with Transformer encoders that can be used in a streaming speech recognition system. Transformer computation blocks based on self-attention are used to encode both audio and label sequences independently. The activations from both audio…
Voice activity detection (VAD) is the task of predicting which parts of an utterance contains speech versus background noise. It is an important first step to determine which samples to send to the decoder and when to close the microphone. The long short-term memory neural network (LSTM) is a popula…